Computing system

ABSTRACT

A system includes a transceiver to communicate with a predetermined target; one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and an edge processing module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target.

This application is related to application Ser. No. 16/558,279, thecontent of which is incorporated by reference.

The present invention relates to computing systems.

2G, 3G and 4G cellular wireless technologies have been mass deployedthroughout the world. Moreover personal area network based technologiessuch as Wi-Fi, Bluetooth and ZigBee have become predominant in our dailylife. 5G is the short form of 5th Generation. It is used to designatefifth generation of mobile technologies. 5G has made it possible to usemobile phone with larger bandwidth possible. It is a packet switchedwireless system. It is used to cover wide area and used to providehigher throughput. It uses CDMA, BDMA and also millimeter wave (forbackhaul wireless connectivity). It uses improved and advanced datacoding/modulation techniques. It provides about 100 Mbps at fullmobility and 1 Gbps at low mobility. It uses smart antenna techniques tosupport higher data rate and coverage.

5G cell phones use radio frequencies in various bands as per countrywise allocations. Typically it uses less than 1 GHz, below 6 GHz andabove 6 GHz (i.e. mmwave) frequency bands. It delivers fastuplink/downlink throughput due to massive MIMO and lower latency between5G network (i.e. SGNB) and itself. The 5G cell phone supports 10 timesthroughput compare to 4G phones. They are backward compatible to 4Gstandards such as LTE and LTE-advanced. Moreover latest 5G phones willsupport Bluetooth, Wi-Fi and NFC based short distance wirelesstechnologies. GPS is also incorporated to support various GPS basedapplications including location tracking, google maps etc.

5G promises an extremely interconnected world where everything fromsmartwatches, vehicles, houses, and farms utilize the ultrafast speedsand low delays it offers. To accomplish this, and to do it well—with aslittle coverage gaps as possible—it's required to have a huge number of5G towers, particularly in areas that demand lots of traffic like bigcities and business districts. Another reason 5G towers have to beinstalled so frequently in busy areas is because for the small cell tosupport superfast speeds, it has to have a direct line of sight with thereceiving device. Since 5G cell towers are so small, they can bepositioned in ordinary places like on light poles, the tops ofbuildings, and even street lights. This translates into less traditionallooking towers but also potentially more eyesores nearly everywhere.

SUMMARY

Various inventive aspects are disclosed below:

Edge Processing with Low Latency

A system includes a cellular transceiver to communicate with apredetermined target; one or more antennas coupled to the 5G transceivereach electrically or mechanically steerable to the predetermined target;a processor to control a directionality of the one or more antennas incommunication with the predetermined target; and an edge processingmodule coupled to the processor and the one or more antennas to providelow-latency computation for the predetermined target.

Implementations can include one or more of the following. The edgeprocessing can include cryogenic processors, quantum processors,neuromorphic processors, learning machines, GPUs, and FPGA, for example.The processor calibrates a radio link between a transceiver in thehousing and a client device. The processor is coupled to fiber opticscable to communicate with a cloud-based radio access network (RAN) or aremote RAN. The processor calibrates a connection by analyzing RSSI andTSSI and moves the antennas until predetermined cellular parameters arereached. The edge processing module comprises at least a processor, agraphical processing unit (GPU), a neural network, a statistical engine,or a programmable logic device (PLD). The edge processing module isembedded in the antenna housing. The edge processing module can be apole, a building, or a light. The cellular transceiver can be a 5Gtransceiver. The processor coordinates beam sweeping by the one or moreantennas with radio nodes or user equipment (UE) devices based uponservice level agreement, performance requirement, traffic distributiondata, networking requirements or prior beam sweeping history. The beamsweeping is directed at a group of autonomous vehicles or a group ofvirtual reality devices. A neural network coupled to a control plane, amanagement plane, and a data plane to optimize 5G parameters. One ormore cameras and sensors in the housing to capture security information.Edge sensors mounted on the housing of the antenna can include LIDAR andRADAR. A camera can perform individual identity identification. The edgeprocessing module streams data to the predetermined target to minimizeloading the target. The edge processing module shares workload with acore processing module located at a head-end and a cloud module locatedat a cloud data center, each processing module having increased latencyand each having a processor, a graphical processing unit (GPU), a neuralnetwork, a statistical engine, or a programmable logic device (PLD). Anedge learning machine in the housing to provide local edge processingfor Internet-of-Things (IOT) sensors with reduced off-chip memoryaccess. The edge learning machine uses pre-trained models and modifiesthe pre-trained models for a selected task. A cellular device for aperson crossing a street near a city light or street light can emit aperson to vehicle (P2V) or a vehicle to person (V2P) safety message. Acloud trained neural network whose network parameters are down-sampledand filter count reduced before transferring to the edge neural network.

Beam Sweeping with Low Latency

A system includes a cellular transceiver to communicate with apredetermined target; one or more antennas coupled to the 5G transceivereach electrically steerable to the predetermined target; and a processorto generate an antenna beam sweeping command based upon trafficdistribution data, device networking requirements or a prior beamsweeping history to focus at least one beam for communication with thepredetermined target.

Implementations can include one or more of the following. The trafficdistribution data may be generated by collecting traffic data from radionodes and/or UE devices, or the networking requirements may include, forexample, service requirements associated with one or more applications(vehicular or reality application) on one or more UE devices. Theprocessor calibrates a radio link between a transceiver in the housingand a client device. The processor is coupled to fiber optics cable tocommunicate with a cloud-based radio access network (RAN) or a remoteRAN. The processor calibrates a connection by analyzing RSSI and TSSIand moves the antennas until predetermined cellular parameters arereached. The cellular transceiver can be a 5G transceiver. The processorcoordinates beam sweeping by the one or more antennas with radio nodesor user equipment (UE) devices based upon service level agreement,performance requirement, traffic distribution data, networkingrequirements or prior beam sweeping history. The beam sweeping isdirected at a group of autonomous vehicles or a group of virtual realitydevices. A neural network (NN) can be used to determine the beamsweeping. The NN can be connected to a control plane, a managementplane, and a data plane to optimize 5G parameters. One or more camerasand sensors in the housing to capture security information. Edge sensorsmounted on the housing of the antenna can include LIDAR and RADAR andsuch data can be sent at top priority to vehicles passing by. A cameracan perform individual identity identification. The edge processingmodule streams data to the predetermined target to minimize loading thetarget. The edge processing module shares workload with a coreprocessing module located at a head-end and a cloud module located at acloud data center, each processing module having increased latency andeach having a processor, a graphical processing unit (GPU), a neuralnetwork, a statistical engine, or a programmable logic device (PLD). Anedge learning machine in the housing to provide local edge processingfor Internet-of-Things (IOT) sensors with reduced off-chip memoryaccess. The edge learning machine uses pre-trained models and modifiesthe pre-trained models for a selected task. A cellular device for aperson crossing a street near a city light or street light can emit aperson to vehicle (P2V) or a vehicle to person (V2P) safety message. Acloud trained neural network whose network parameters are down-sampledand filter count reduced before transferring to the edge neural network.An edge processing module can be connected to the processor and the oneor more antennas to provide low-latency computation for thepredetermined target. The edge processing module can be at least aprocessor, a graphical processing unit (GPU), a neural network, astatistical engine, or a programmable logic device (PLD). The edgeprocessing module is embedded in the antenna housing. The edgeprocessing module can be part of a pole, a building, or a light. Theprocessor can run code including requesting a portion of a network for agroup of devices, checking for available resources to satisfy therequest and assigning a network slice deployment layout satisfying therequested portion of the network including antenna level layout, andmanaging resources at the antenna level as part of the requested portionof the network to provide communication for the group. The request canbe for enhanced services for autonomous vehicles. The request can be forreality applications such as virtual reality or augmented reality. Uponrequest, the system determines a candidate network slice deploymentlayout that satisfies the network level requirements and network layoutsof a service request and costs associated with a candidate networkresource and/or a candidate network slice deployment layout, which isused as a basis to optimally use available network resources. The systemcoordinates, authorizes, releases and/or engages network resources innetwork. The system obtains network slice deployment layout descriptorscorresponding to a network slice deployment layout and the system maymanage the provisioning of the network slice deployment layout tosatisfy the service request. The system may perform various functionssuch as, for example, network slice life cycle management, configurationmanagement (e.g., policies, isolation of management), performancemanagement (e.g., service level agreement (SLA) management, serviceassurance and programmability), service mapping. The system updates inreal time network resource device regarding availability of networkresources based on the current state of network resources in network andprovisioned network resources that support service requests. The systemcan respond to the request with a monetary cost associated with acandidate network slice deployment layout, quality-of-service valuesassociated with the candidate network slice deployment layout (e.g.,minimum value and/or maximum value pertaining to latency, bandwidth,reliability, etc.) and/or other information representative of theconfiguration and/or service such as virtual network resource,non-virtual network resource, cloud, 5G RAN access, for example. Themethod includes storing network resource and capability informationpertaining to network resources of a network and generating networklevel requirement information that would support the network service.The method includes creating end-to-end network slice deploymentinformation that includes parameters to provision an end-to-end networkslice deployment layout in the network that supports the networkservice.

Network Slicing for Groups of Devices

A method to manage a cellular network includes requesting a portion of anetwork for a group of devices, checking for available resources tosatisfy the request and assigning a network slice deployment layoutsatisfying the requested portion of the network including antenna levellayout, and managing resources at the antenna level as part of therequested portion of the network to provide communication for the group.

In one implementation, the request can be for enhanced services forautonomous vehicles. The request can be for reality applications such asvirtual reality or augmented reality. Upon request, the systemdetermines a candidate network slice deployment layout that satisfiesthe network level requirements and network layouts of a service requestand costs associated with a candidate network resource and/or acandidate network slice deployment layout, which is used as a basis tooptimally use available network resources. The system coordinates,authorizes, releases and/or engages network resources in network. Thesystem obtains network slice deployment layout descriptors correspondingto a network slice deployment layout and the system may manage theprovisioning of the network slice deployment layout to satisfy theservice request. The system may perform various functions such as, forexample, network slice life cycle management, configuration management(e.g., policies, isolation of management), performance management (e.g.,service level agreement (SLA) management, service assurance andprogrammability), service mapping. The system updates in real timenetwork resource device regarding availability of network resourcesbased on the current state of network resources in network andprovisioned network resources that support service requests. The systemcan respond to the request with a monetary cost associated with acandidate network slice deployment layout, quality-of-service valuesassociated with the candidate network slice deployment layout (e.g.,minimum value and/or maximum value pertaining to latency, bandwidth,reliability, etc.) and/or other information representative of theconfiguration and/or service such as virtual network resource,non-virtual network resource, cloud, 5G RAN access, for example. Themethod includes storing network resource and capability informationpertaining to network resources of a network and generating networklevel requirement information that would support the network service.The method includes creating end-to-end network slice deploymentinformation that includes parameters to provision an end-to-end networkslice deployment layout in the network that supports the networkservice.

Steerable Actuated Antenna

A system, includes a moveable surface; and one or more antennas mountedon the moveable surface to change a direction of the antenna to apredetermined target. The system may include one or more of thefollowing. A pneumatic actuator or electrical motor can be placed underprocessor control to change the curvature of the lens and to change thedirectionality of the antenna. The processor can calibrate the RF linkbetween the tower and the client device. The processor can calibrate theconnection by examining the RSSI and TSSI and scan the moveable surfaceuntil the optimal RSSI/TSSI levels (or other cellular parameters) arereached. The scanning of the target device can be done by moving theactuators up or down. Opposing actuator arrays can be formed to providetwo-sided communication antennas. An array of actuators can be used(similar to bee eyes), each antenna is independently steerable tooptimize 5G transmission. Fresnel lens can be used to improve SNR. Thefocusing of the actuators can be automatically done using processor withiterative changes in the orientation of the antenna by changing theactuators until predetermined criteria is achieved such as the besttransmission speed, TSSI, RSSI, SNR, among others. This is similar tothe way human vision eyeglass correction is done. A learning machinesuch as neural network or SVM can be used over the control/managementplane of the 5G network to optimize 5G parameters based on localbehaviors.

Learning System Plane

A system to optimize data flow in a 5G network, includes a neuralnetwork plane; a control plane coupled to the neural network plane; amanagement plane coupled to the neural network plane; a data planecoupled to the neural network plane, wherein the neural network planereceives cellular network statistics from the data plane for training,and during run-time, the neural network provides operating parameters tothe data, control and management planes; and one or more operationssending resource request to the neural network plane for autonomousresolution that maximizes data flow in the system. The system mayinclude one or more of the following: A moveable surface; and one ormore antennas mounted on the moveable surface to change a direction ofthe antenna to a predetermined target. A pneumatic actuator orelectrical motor can be placed under processor control to change thecurvature of the lens and to change the directionality of the antenna.The processor can calibrate the RF link between the tower and the clientdevice. The processor can calibrate the connection by examining the RSSIand TSSI and scan the moveable surface until the optimal RSSI/TSSIlevels (or other cellular parameters) are reached. The scanning of thetarget device can be done by moving the actuators up or down. Opposingactuator arrays can be formed to provide two-sided communicationantennas. An array of actuators can be used (similar to bee eyes), eachantenna is independently steerable to optimize 5G transmission. Fresnellens can be used to improve SNR. Focus the antenna on BS and UE, andthen combine antennas for orthogonal transmissions based on variousfactors. The focusing of the actuators can be automatically done usingprocessor with iterative changes in the orientation of the antenna bychanging the actuators until predetermined criteria is achieved such asthe best transmission speed, TSSI, RSSI, SNR, among others. This issimilar to the way human vision eyeglass correction is done.

Man-Hole Antenna

A system, comprising one or more actuators; a ground cover above the oneor more actuators providing a moveable surface, wherein the actuatorsmove to adjust the curvature of the movable surface; and an antennamounted on the moveable surface to change a direction of the antenna toa predetermined target.

A system, includes a ground cover (such as a manhole cover) that allowsradio signal to pass through; a moveable surface coupled to the cover;and one or more antennas mounted on the moveable surface to change adirection of the antenna to a predetermined target.

3G/4G Cell Towers

A system includes a cell tower with a pole and a top portion to mount 4Gantennas and a 5G housing; one or more mechanically steerable activeantennas mounted on the 5G housing and in communication with apredetermined target using 5G protocols. The system may include one ormore of the following. A processor can control to change the curvatureof the surface and/or to change the directionality of the antenna. Theprocessor can calibrate the RF link between the tower and the clientdevice. The processor can calibrate the connection by examining the RSSIand TSSI and scan the moveable lens until the optimal RSSI/TSSI levels(or other cellular parameters) are reached. The scanning of the targetclient/device can be done by injecting or removing liquid from moveablesurface, or can be done by moving actuators coupled to the surface.Opposing pairs of lenses can be formed to provide two-sidedcommunication antennas. An array of actuator/antenna can be used(similar to bee eyes), each antenna is independently steerable tooptimize 5G transmission. Fresnel lens can be used to improve SNR. Thefocusing of the 5G signals to the target client/device can beautomatically done using processor with iterative changes in theorientation of the antenna by changing the curvature or shape of thesurface until predetermined criteria is achieved such as the besttransmission speed, TSSI, RSSI, SNR, among others. A learning machinesuch as neural network or SVM can be used over the control/managementplane of the 5G network to optimize 5G parameters based on localbehaviors. A movable surface can be provided on the housing to steer theantenna. The moveable surface can be liquid lens or actuator array asdescribed above. Cameras and sensors can be positioned to capturesecurity information. Learning machine hardware can provide localprocessing at the edge.

Actuator-Based Active Antenna Array

An antenna, includes an array of antenna element, each connected to aseparate transceiver; an array of actuators to point the antennaelements; data converters coupled to the transceivers for up conversionand down conversion; a baseband unit (BBU) with one or more digitalsignal processors coupled to the data converters; and a broadbandconnection connecting the baseband unit to a wide area network (WAN).The system may include one or more of the following. A processor cancontrol to change the curvature of the surface and/or to change thedirectionality of the antenna. The processor can calibrate the RF linkbetween the tower and the client device. The processor can calibrate theconnection by examining the RSSI and TSSI and scan the moveable lensuntil the optimal RSSI/TSSI levels (or other cellular parameters) arereached. The scanning of the target client/device can be done byinjecting or removing liquid from moveable surface, or can be done bymoving actuators coupled to the surface. Opposing pairs of lenses can beformed to provide two-sided communication antennas. An array ofactuator/antenna can be used (similar to bee eyes), each antenna isindependently steerable to optimize 5G transmission. Fresnel lens can beused to improve SNR. The focusing of the 5G signals to the targetclient/device can be automatically done using processor with iterativechanges in the orientation of the antenna by changing the curvature orshape of the surface until predetermined criteria is achieved such asthe best transmission speed, TSSI, RSSI, SNR, among others.

A learning machine such as neural network or SVM can be used over thecontrol/management plane of the 5G network to optimize 5G parametersbased on local behaviors. The learning machine can be used to helpsteering the antennas to improve connections with UEs. The learningmachine can also optimize operation based on data collected from otherelements in the transceiver and/or the BBU. The broadband connection canbe fiber optic or wireless connection (UWB). The baseband unit can havea high-speed serial link as defined by the Common Public Radio Interface(CPRI), Open Base Station Architecture Initiative (OBSAI), or Open RadioInterface (ORI). The high speed serial link is used to transport the Txand Rx signals from the BBU to the antennas. The AAS can have passivecooling fins on the housing, or can use evaporative cooling techniques,for example with an enhanced boiling or evaporation microstructuresurface including microporous structures; and an electro-depositedsurface to enhance a vapor condensation rate, wherein the surfaceincludes a porous medium to replenish condensed liquid back to themicrostructure surface by capillary pumping force, wherein the surfaceis part of an antenna. Since there are many more transceivers/amplifiersin an AAS, each amplifier in an AAS delivers a much lower power whencompared to an amplifier in an equivalent RRH.

Beamforming Actuator Driven Active Antenna to Track Moving Ues

A method of communicating data with a UE using an array antenna onboarda cell tower and having a digital beam former (DBF), said array antennahaving a plurality of actuators moving the RF radiating elements forproviding steerable antenna beams within an antenna footprint region,said DBF providing for each radiating element, beam forming coefficientsfor controlling characteristics of said steerable antenna beams. Themethod includes receiving a signal from the UE within a receive one ofsaid steerable antenna beams; determining a location direction of the UEusing said signal; generating digital beam forming coefficients toprovide a transmit one of said steerable antenna beams in said locationdirection of the UE; transmitting data from said cell tower to said UEwithin said one transmit steerable antenna beam; tracking said locationdirection of said UI as said cell tower and said UE move relative toeach other; adjusting said beam forming coefficients associated with onetransmit steerable antenna beam in response to the tracking step tomaintain said one transmit steerable antenna beam in the locationdirection of said UE; further adjusting said beam forming coefficientsassociated with one transmit steerable antenna beam to improve a signalquality of communication signal received at said communication station.The system may include one or more of the following. The antenna arrayscan have shape shifting or moving surfaces to directionally aim theantennas. The system remaps the beams to avoid obstructions or issuesthat affect 5G/6G transmissions. The Beams can also be changed accordingto load, usage, time of day, or other factors. The processor cancalibrate the connection by examining the RSSI and TSSI and scan theantenna actuators or moveable lens until the optimal RSSI/TSSI levels(or other cellular parameters) are reached. The scanning of the targetclient/device can be done by injecting or removing liquid from moveablesurface, or can be done by moving actuators coupled to the surface.Opposing pairs of lenses can be formed to provide two-sidedcommunication antennas. An array of actuator/antenna can be used(similar to bee eyes), each antenna is independently steerable tooptimize 5G transmission. Fresnel lens can be used to improve SNR. Thefocusing of the 5G signals to the target client/device can beautomatically done using processor with iterative changes in theorientation of the antenna by changing the curvature or shape of thesurface until predetermined criteria is achieved such as the besttransmission speed, TSSI, RSSI, SNR, among others.

A learning machine such as neural network or SVM can be used over thecontrol/management plane of the 5G network to optimize 5G parametersbased on local behaviors. The learning machine can be used to helpsteering the antennas to improve connections with UEs. The learningmachine can also optimize operation based on data collected from otherelements in the transceiver and/or the BBU. The broadband connection canbe fiber optic or wireless connection (UWB). The baseband unit can havea high-speed serial link as defined by the Common Public Radio Interface(CPRI), Open Base Station Architecture Initiative (OBSAI), or Open RadioInterface (ORI). The high speed serial link is used to transport the Txand Rx signals from the BBU to the antennas. The AAS can have passivecooling fins on the housing, or can use evaporative cooling techniques,for example with an enhanced boiling or evaporation microstructuresurface including microporous structures; and an electro-depositedsurface to enhance a vapor condensation rate, wherein the surfaceincludes a porous medium to replenish condensed liquid back to themicrostructure surface by capillary pumping force, wherein the surfaceis part of an antenna. Since there are many more transceivers/amplifiersin an AAS, each amplifier in an AAS delivers a much lower power whencompared to an amplifier in an equivalent RRH. Once the learning machinedetermines the beam sweeping patterns for antenna beams of radio nodesand/or UE devices, beam sweeping commands may be provided to individualradio nodes and/or UE devices via core network and mobile backhaulnetwork. Radio nodes may forward beam sweeping commands to respective UEdevices over a wireless channel (e.g., a wireless control channel).Additionally, network control device may prioritize particular antennabeams, where high priority beams are reserved to service users havinghigh networking requirements. In an embodiment, high priority beams maybe classified as “active” beams. Beams having lower priority than activebeams may be classified as “candidate” beams, which may be selected toreplace active beams if necessary. Beams having lower priority thanactive and candidate beans may be classified as “alternative” beams,which may be used as backup beams in case an active beam is temporarilyblocked and a suitable candidate beam is unavailable. In addition, thepriority of beams may be updated according to the time of day,particular days or dates (e.g., workdays, weekends, holidays, etc.),and/or the time of season (to account for seasonal effects ofpropagation, seasonal variations of the density of users, and/orvariations in objects which may block signal propagation). In addition,network control device may also use prior knowledge of prior beamsweeping patterns to influence the determination of current and/orfuture beam sweeping patterns. Moreover, the beam sweeping patternsassociated with control signaling broadcast between radio nodes and UEdevices may be adjusted differently than antenna beams associated withdata bearing channels. Additionally, differences between beam sweepingpatterns may be based on the beam width of individual antenna beamsand/or the number of beam sweeping positions.

Multi-Level 5G/6G Antenna

An antenna system, includes a high power active antenna array mounted ona cell tower, balloon, or a drone, the high power active antenna arraycontrolled by a BBU with a broadband connection; a plurality of mediumpower active antenna arrays wirelessly coupled to the high power activeantenna, wherein the medium power antenna array relays data transmissionbetween the high power active antenna array and a UE to reduce RFexposure on biologics. This reduces cancer risk on users. The system mayinclude one or more of the following: The antenna arrays can have shapeshifting or moving surfaces to directionally aim the antennas. The highpower active antenna can have an array of antenna element, eachconnected to a separate transceiver; an array of actuators to point theantenna elements; data converters coupled to the transceivers for upconversion and down conversion; the baseband unit (BBU) with one or moredigital signal processors coupled to the data converters. The processorcan calibrate the connection by examining the RSSI and TSSI and scan themoveable lens until the optimal RSSI/TSSI levels (or other cellularparameters) are reached. The scanning of the target client/device can bedone by injecting or removing liquid from moveable surface, or can bedone by moving actuators coupled to the surface. Opposing pairs oflenses can be formed to provide two-sided communication antennas. Anarray of actuator/antenna can be used (similar to bee eyes), eachantenna is independently steerable to optimize 5G transmission. Fresnellens can be used to improve SNR. The focusing of the 5G signals to thetarget client/device can be automatically done using processor withiterative changes in the orientation of the antenna by changing thecurvature or shape of the surface until predetermined criteria isachieved such as the best transmission speed, TSSI, RSSI, SNR, amongothers.

A learning machine such as neural network or SVM can be used over thecontrol/management plane of the 5G network to optimize 5G parametersbased on local behaviors. The learning machine can be used to helpsteering the antennas to improve connections with UEs. The learningmachine can also optimize operation based on data collected from otherelements in the transceiver and/or the BBU. The broadband connection canbe fiber optic or wireless connection (UWB). The baseband unit can havea high-speed serial link as defined by the Common Public Radio Interface(CPRI), Open Base Station Architecture Initiative (OBSAI), or Open RadioInterface (ORI). The high speed serial link is used to transport the Txand Rx signals from the BBU to the antennas. The AAS can have passivecooling fins on the housing, or can use evaporative cooling techniques,for example with an enhanced boiling or evaporation microstructuresurface including microporous structures; and an electro-depositedsurface to enhance a vapor condensation rate, wherein the surfaceincludes a porous medium to replenish condensed liquid back to themicrostructure surface by capillary pumping force, wherein the surfaceis part of an antenna. Since there are many more transceivers/amplifiersin an AAS, each amplifier in an AAS delivers a much lower power whencompared to an amplifier in an equivalent RRH.

The medium power antenna arrays can be mounted on traffic lights orstreet lights as replacement lights with 5G relay capacity, and furthercan provide fast response time for vehicular navigation/control. Themedium power antenna arrays can be mounted on car, bus, trucks, drones,local stores, mailboxes. The host for the medium power antenna cancollect a usage fee in exchange.

Car/Truck/Van/Bus/Vehicle with 5G Antenna Small Cells

A system, includes a moveable vehicle including a pole and a top portionto mount 4G antennas and a 5G housing, wherein the pole is retractableand extendable during 5G operation; one or more antennas mounted on the5G housing and in communication with a predetermined target using 5Gprotocols. The system may include one or more of the following: Aprocessor can control to change the curvature of the surface and/or tochange the directionality of the antenna. The processor can calibratethe RF link between the tower and the client device. The processor cancalibrate the connection by examining the RSSI and TSSI and scan themoveable lens until the optimal RSSI/TSSI levels (or other cellularparameters) are reached. The scanning of the target client/device can bedone by injecting or removing liquid from moveable surface, or can bedone by moving actuators coupled to the surface. Opposing pairs oflenses can be formed to provide two-sided communication antennas. Anarray of actuator/antenna can be used (similar to bee eyes), eachantenna is independently steerable to optimize 5G transmission. Fresnellens can be used to improve SNR. The focusing of the 5G signals to thetarget client/device can be automatically done using processor withiterative changes in the orientation of the antenna by changing thecurvature or shape of the surface until predetermined criteria isachieved such as the best transmission speed, TSSI, RSSI, SNR, amongothers. A learning machine such as neural network or SVM can be usedover the control/management plane of the 5G network to optimize 5Gparameters based on local behaviors. A movable surface can be providedon the housing to steer the antenna. The moveable surface can be liquidlens or actuator array as described above. Cameras and sensors can bepositioned to capture security information. Learning machine hardwarecan provide local processing at the edge. A frame can be used with anantenna support structure having means to permit its collapsing and awaveguide antenna mounted to said support structure and including aplurality of integrally connected tubular waveguide cells that form acell array that focuses transmitted signals onto a signal processingdevice; said lens waveguide antenna having means to permit itscollapsing and a second support structure mount that operativelyconnects said collapsible support structure to a mounting surface tocorrectly position said collapsible lens waveguide antenna relative tosaid signal processing device when said antenna is operationallydeployed. A fleet of drones can operate and navigate as a flock of birdsto provide real time adjustment in coverage as needed. The flock ofbirds antenna has power and autonomous navigation and can self-assembleand scatter as needed to avoid physical and wireless communicationobstacles.

Glider/Helicopter/Balloon/Ship/Leo Drone with 5G Antenna

A system, includes an airborne frame to mount 4G antennas and a 5Ghousing; one or more antennas mounted on the 5G housing and incommunication with a predetermined target using 5G protocols.

The system may include one or more of the following: A processor cancontrol to change the curvature of the surface and/or to change thedirectionality of the antenna. The processor can calibrate the RF linkbetween the tower and the client device. The processor can calibrate theconnection by examining the RSSI and TSSI and scan the moveable lensuntil the optimal RSSI/TSSI levels (or other cellular parameters) arereached. The scanning of the target client/device can be done byinjecting or removing liquid from moveable surface, or can be done bymoving actuators coupled to the surface. Opposing pairs of lenses can beformed to provide two-sided communication antennas. An array ofactuator/antenna can be used (similar to bee eyes), each antenna isindependently steerable to optimize 5G transmission. Fresnel lens can beused to improve SNR. The focusing of the 5G signals to the targetclient/device can be automatically done using processor with iterativechanges in the orientation of the antenna by changing the curvature orshape of the surface until predetermined criteria is achieved such asthe best transmission speed, TSSI, RSSI, SNR, among others. A learningmachine such as neural network or SVM can be used over thecontrol/management plane of the 5G network to optimize 5G parametersbased on local behaviors. A movable surface can be provided on thehousing to steer the antenna. The moveable surface can be liquid lens oractuator array as described above. Cameras and sensors can be positionedto capture security information. Learning machine hardware can providelocal processing at the edge. The air frame has an antenna supportstructure having means to permit its collapsing and a waveguide antennamounted to said support structure and including a plurality ofintegrally connected tubular waveguide cells that form a cell array thatfocuses transmitted signals onto a signal processing device; said lenswaveguide antenna having means to permit its collapsing and a secondsupport structure mount that operatively connects said collapsiblesupport structure to a mounting surface to correctly position saidcollapsible lens waveguide antenna relative to said signal processingdevice when said antenna is operationally deployed. A fleet of dronescan operate and navigate as a flock of birds to provide real timeadjustment in coverage as needed. The flock of birds antenna has powerand autonomous navigation and can self-assemble and scatter as needed toavoid physical and wireless communication obstacles. Thecars/trucks/buses can carry ads as a monetization system. Alternatively,personal vehicles can be paid a percentage of the traffic relayed bytheir vehicles.

Cell Phone Antenna

A system, includes a cell phone housing; and one or more antennasmounted on the housing, the antenna being selectable to avoiddischarging RF energy into a human body and to target RF energy at apredetermined target. The system may include one or more of thefollowing: A processor can control to change the directionality of theantenna. The processor can calibrate the RF link between the tower andthe client device. The processor can calibrate the connection byexamining the RSSI and TSSI and scan the moveable lens until the optimalRSSI/TSSI levels (or other cellular parameters) are reached. Thescanning of the target client/device can be done by injecting orremoving liquid from moveable surface, or can be done by movingactuators coupled to the surface. Opposing pairs of lenses can be formedto provide two-sided communication antennas. An array ofactuator/antenna can be used (similar to bee eyes), each antenna isindependently steerable to optimize 5G transmission. Fresnel lens can beused to improve SNR. The focusing of the 5G signals to the targetclient/device can be automatically done using processor with iterativechanges in the orientation of the antenna by changing the curvature orshape of the surface until predetermined criteria is achieved such asthe best transmission speed, TSSI, RSSI, SNR, among others. A learningmachine such as neural network or SVM can be used over thecontrol/management plane of the 5G network to optimize 5G parametersbased on local behaviors. A processor controlled moveable surface can beprovided on the tree (such as a leaf, flower, or fruit on the tree),wherein the moveable surface can be liquid lens or actuators that movethe surface as detailed above. Cameras and sensors can be positioned tocapture security information. Learning machine hardware can providelocal processing at the edge.

Powering of IOT Devices Using 5G Energy

An IOT system, includes a housing having a moveable surface; one or moreantennas mounted on a moveable surface, wherein the antenna direction ischanged by the moveable surface to receive RF energy from a small cell;a capacitor, battery or energy storage device coupled to the antennas tostore received energy; and a power regulator coupled to the capacitor,battery, or energy storage A processor can control to change thedirectionality of the antenna. The processor can calibrate the RF linkbetween the tower and the client device. The processor can calibrate theconnection by examining the RSSI and TSSI and scan the moveable lens oractuators until the optimal RSSI/TSSI levels (or other cellularparameters) are reached. The scanning of the target client/device can bedone by injecting or removing liquid from moveable surface, or can bedone by moving actuators coupled to the surface. Opposing pairs oflenses can be formed to provide two-sided communication antennas. Anarray of actuator/antenna can be used (similar to bee eyes), eachantenna is independently steerable to optimize 5G transmission. Fresnellens can be used to improve SNR. The focusing of the 5G signals to thetarget client/device can be automatically done using processor withiterative changes in the orientation of the antenna by changing thecurvature or shape of the surface until predetermined criteria isachieved such as the best transmission speed, TSSI, RSSI, SNR, amongothers. A learning machine such as neural network or SVM can be usedover the control/management plane of the 5G network to optimize 5Gparameters based on local behaviors. A processor controlled moveablesurface can be provided on the tree (such as a leaf, flower, or fruit onthe tree), wherein the moveable surface can be liquid lens or actuatorsthat move the surface as detailed above. Cameras and sensors can bepositioned to capture security information. Learning machine hardwarecan provide local processing at the edge.

Antenna with Evaporative Cooling

A heat spreader to cool a heated region of a device, includes anenhanced boiling or evaporation microstructure surface includingmicroporous structures; and an electro-deposited surface to enhance avapor condensation rate, wherein the surface includes a porous medium toreplenish condensed liquid back to the microstructure surface bycapillary pumping force, wherein the surface is part of an antenna. Inimplementations, each surface comprises a plate. The electro-depositedsurfaces utilize boiling (evaporation), condensation, and capillarypumping action. Liquid is vaporized or boiled from the electro-depositedsurface designed for boiling (evaporation) enhancement. Vapor iscondensed at the enhanced surface for condensation. The enhanced surfacefor condensation is formed by electro-deposition. Condensed liquid issupplied back to the heated region by another electro-deposited surfaceaimed for capillary pumping action. One or more structures are mountedon at least one of the opposing surfaces. The first and second opposingsurfaces are separated by a small gap. The first and second opposingsurface have a first separation distance above a predetermined region ondevice and a second separation distance surrounding the predeterminedregion and wherein the second separation distance is larger than thefirst separation distance.

A method to cool an electronic device includes forming an enhancedboiling or evaporation surface including microporous structures; formingan electro-deposited surface to improve a condensation rate of vapor,wherein the surface includes a porous medium to replenish condensedliquid back to the first surface by capillary pumping force; andcommunicating radio signal using the other side of the condensationsurface. In implementation, the method includes improving a heattransfer coefficient in a low-profile vapor chamber using wickstructures having projections formed by electrodepositing a metal on thetarget surfaces at a first current density followed by strengthening atone or more second current densities lower than the first currentdensity. Projections are formed with nucleate boiling cavities on thesurface from local heating source, increased capillary pumping action ofthe wick structure, and augmented condensation rate of vapors. Theelectronic device is cooled using a combination of nucleate boiling(evaporation), capillary pumping action, and condensation. A two-phasecooling chamber is formed with integrated electro-deposited surfacesthat utilize boiling (evaporation), condensation, and capillary pumpingaction. The method, includes vaporizing a liquid into a vapor from aheated region thermally coupled to a first electro-deposited surfacedesigned for boiling (evaporation) enhancement; condensing the vapor atan enhanced surface for condensation by a second electro-depositedsurface; returning the condensed liquid to the heated region throughcapillary pumping action by the second electro-deposited surface. Themethod includes enclosing both surfaces in a thin circular, square, orrectangular housing for heat spreading. One or more supportingstructures are formed on one surface to provide mechanical strength thatprevents bending of the surface and structure built thereon.

A streamlined flow pattern is induced by nucleate boiling with apredetermined structure shape. A two-phase cooling chamber to cool aheated region, includes an enhanced boiling or evaporationmicrostructure surface including microporous structures; and anelectro-deposited surface to enhance a vapor condensation rate, whereinthe surface includes a porous medium to replenish condensed liquid backto the microstructure surface by capillary pumping force, wherein theelectro-deposited surfaces utilize boiling or evaporation, condensation,and capillary pumping action. Liquid is vaporized or boiled from theelectro-deposited surface designed for boiling or evaporationenhancement, then the vapor will be condensed at the enhanced surfacefor condensation and wherein the condensed liquid is supplied back tothe heated region by an electro-deposited surface aimed for capillarypumping action.

Low Orbit Drone with Active Antennas

A system, includes an airborne frame to mount 4G antennas and a 5Ghousing; a variable buoyancy propulsion with a combination of a lighterthan air chamber and a compressed gas chamber to propel the airborneframe; and one or more antennas mounted on the 5G housing and incommunication with a predetermined target using 5G protocols. The systemmay include one or more of the following: A processor can control tochange the curvature of the surface and/or to change the directionalityof the antenna. The processor can calibrate the RF link between thetower and the client device. The processor can calibrate the connectionby examining the RSSI and TSSI and scan the moveable lens until theoptimal RSSI/TSSI levels (or other cellular parameters) are reached. Thescanning of the target client/device can be done by injecting orremoving liquid from moveable surface, or can be done by movingactuators coupled to the surface. Opposing pairs of lenses can be formedto provide two-sided communication antennas. An array ofactuator/antenna can be used (similar to bee eyes), each antenna isindependently steerable to optimize 5G transmission. Fresnel lens can beused to improve SNR. The focusing of the 5G signals to the targetclient/device can be automatically done using processor with iterativechanges in the orientation of the antenna by changing the curvature orshape of the surface until predetermined criteria is achieved such asthe best transmission speed, TSSI, RSSI, SNR, among others. A learningmachine such as neural network or SVM can be used over thecontrol/management plane of the 5G network to optimize 5G parametersbased on local behaviors. A movable surface can be provided on thehousing to steer the antenna. The moveable surface can be liquid lens oractuator array as described above. Cameras and sensors can be positionedto capture security information. Learning machine hardware can providelocal processing at the edge. The air frame has an antenna supportstructure having means to permit its collapsing and a waveguide antennamounted to said support structure and including a plurality ofintegrally connected tubular waveguide cells that form a cell array thatfocuses transmitted signals onto a signal processing device; said lenswaveguide antenna having means to permit its collapsing and a secondsupport structure mount that operatively connects said collapsiblesupport structure to a mounting surface to correctly position saidcollapsible lens waveguide antenna relative to said signal processingdevice when said antenna is operationally deployed. A fleet of dronescan operate and navigate as a flock of birds to provide real timeadjustment in coverage as needed. The flock of birds antenna has powerand autonomous navigation and can self-assemble and scatter as needed toavoid physical and wireless communication obstacles. A refueling dronecan be used to supply the GBS with power by swap battery with the GBS orrefueling the hydrogen fuel cells, where the refueling drone designedfor boom-type transfers in which a boom controller extends and maneuversa boom to establish a connection to transfer hydrogen fuel from therefueling drone to the refueling drone. Prior to refueling, therefueling drone extends a refueling probe. The refueling drone includesa navigation system that may be used for positioning the refueling droneduring aerial refueling. The GBS navigation system provides inertial andGlobal Positioning System (GPS) measurement data to the refueling dronevia a data link. Relative positioning can be used to navigate bothcrafts.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1E show an exemplary 5G network architecture, while FIGS. 1F-1Gshow exemplary 5G mobile devices.

FIGS. 2A-2B show an exemplary city light small cell environment withcrime/pollution sniffing capabilities.

FIG. 2C shows a ground based, light based, and plant based antennanetwork.

FIG. 2D shows an exemplary security camera with small cell and antennas.

FIG. 2E shows an exemplary man-hole cover with a small cell andsteerable antennas.

FIG. 2F shows an exemplary 4G-5G network in accordance with one aspect.

FIG. 2G shows vehicles for 5G operations.

FIGS. 2H-2I show exemplary antenna systems.

FIG. 2J shows an exemplary edge processing system.

FIG. 2K shows exemplary vehicles that can be used to supplement 5Gservices as mobile 5G cell towers.

FIGS. 3A-3B show an exemplary antenna element.

FIG. 4A shows an exemplary array of antennas.

FIG. 4B-4C shows an exemplary lens-based array antenna whose orientationcan be controlled.

FIGS. 4D-4E shows an array of actuators to point an antenna element in aselected direction.

FIGS. 5A-5B show exemplary cooler and antenna combination for 5Gelectronic chips.

FIG. 6 shows an exemplary neural MIMO system.

FIGS. 7A-7D show exemplary learning machine processes and architectures.

FIG. 8 shows an exemplary hybrid classical/quantum computer supportingedge computing near the antenna towers.

DESCRIPTION

FIGS. 1A-1D shows an exemplary 5G network architecture. A plurality ofphones running 2G, 3G, 4G and 5G communication with wireless RANs. Theradio access network (RAN) has been in use since the beginning ofcellular technology and has evolved through the generations of mobilecommunications (1G, 2G, 3G, 4G, and in anticipation of the forthcoming5G). Components of the RAN include a base station and antennas thatcover a given region depending on their capacity. In a RAN, radio sitesprovide radio access and coordinate management of resources across theradio sites. A device is wirelessly connected to the core network, andthe RAN transmits its signal to various wireless endpoints, and thesignal travels with other networks' traffic. Two types of radio accessnetworks are Generic Radio Access Network (GRAN), which uses basetransmission stations and controllers to manage radio links forcircuit-switched and packet-switched core networks; and GSM Edge RadioAccess Network (GERAN), which supports real-time packet data. Two othertypes of radio access networks are UMTS Terrestrial Radio Access Network(UTRAN), which supports both circuit-switched and packet-switchedservices; and Evolved Universal Terrestrial Radio Access Network(E-UTRAN), which focuses only on packet-switched services. E-UTRAN alsoprovides high data rates and low latency. The RAN's controller controlsthe nodes that are connected to it. The network controller performsradio resource management, mobility management, and data encryption. Itconnects to the circuit-switched core network and the packet-switchedcore network, depending on the type of RAN. The RAN architecturesseparate the user plane from the control plane into different networkelements. In this scenario, the RAN controller can exchange user datamessages through one software-defined networking (SDN) switch, and asecond set with base stations via a second control-based interface. Thisseparation of the control plane and data plane will be an essentialaspect of the flexible 5G radio access network, as it aligns with SDNand network functions virtualization (NFV) techniques such as servicechaining and network slicing.

In one implementation of one or more gNBs and one or more UEs in whichsystems and methods for supporting ultra-reliable low-latencycommunication (URLLC) service and associated numerologies in fifthgeneration (5G) New Radio (NR) may be implemented. The one or more UEscommunicate with one or more gNBs using one or more physical antennas.For example, a UE transmits electromagnetic signals to the gNB andreceives electromagnetic signals from the gNB using the one or morephysical antennas. The gNB communicates with the UE using one or morephysical antennas.

The UE and the gNB may use one or more channels and/or one or moresignals to communicate with each other. For example, the UE may transmitinformation or data to the gNB using one or more uplink channels.Examples of uplink channels include a physical shared channel (e.g.,PUSCH (Physical Uplink Shared Channel)), and/or a physical controlchannel (e.g., PUCCH (Physical Uplink Control Channel)), etc. The one ormore gNBs may also transmit information or data to the one or more UEsusing one or more downlink channels, for instance. Examples of downlinkchannels physical shared channel (e.g., PDSCH (Physical Downlink SharedChannel), and/or a physical control channel (PDCCH (Physical DownlinkControl Channel)), etc. Other kinds of channels and/or signals may beused.

Each of the one or more UEs may include one or more transceivers, one ormore demodulators, one or more decoders, one or more encoders, one ormore modulators, a data buffer and a UE operations module. For example,one or more reception and/or transmission paths may be implemented inthe UE. The transceiver may include one or more receivers and one ormore transmitters. The one or more receivers may receive signals fromthe gNB using one or more antennas. For example, the receiver 120 mayreceive and downconvert signals to produce one or more received signals.The one or more received signals may be provided to a demodulator. Theone or more transmitters may transmit signals to the gNB using one ormore physical antennas. For example, the one or more transmitters mayupconvert and transmit one or more modulated signals.

The demodulator may demodulate the one or more received signals toproduce one or more demodulated signals. The one or more demodulatedsignals may be provided to the decoder. The UE may use the decoder todecode signals. The decoder may produce decoded signals, which mayinclude a UE-decoded signal (also referred to as a first UE-decodedsignal). For example, the first UE-decoded signal may comprise receivedpayload data, which may be stored in a data buffer. Another signalincluded in the decoded signals (also referred to as a second UE-decodedsignal) may comprise overhead data and/or control data. For example, thesecond UE-decoded signal may provide data that may be used by the UEoperations module to perform one or more operations. In general, the UEoperations module may enable the UE to communicate with the one or moregNBs. The UE operations module may include one or more of a UE URLLCmodule. With regard to NR, some considerations with SR include trafficcharacteristics, logical channel/logical channel group, the amount ofdata available, information related to numerology and/or TransmissionTime Interval (TTI) duration, and the priority of data.

Short latency in NR may be important to support services like URLLC.This may impact the design of the SR. The design of the SR in amulti-numerology/TTI duration configuration also influences the latency.With regard to NR, some considerations for SR latency and periodicityinclude: major design changes related to SR latency and periodicitycompared to LTE; what is the impact from the NR latency requirements;what is the impact from a multiple numerology/TTI durationconfiguration; and what is the impact from other functions designed toreduce latency (e.g., grant-free transmissions and Semi-PersistentScheduling (SPS)).

The function of the Buffer Status Report (BSR) in LTE is for the UE toreport the amount of available data in the UE to the eNB. The eNB canthen use this information to set the size of the UL grant. Logicalchannels are grouped together in logical channel groups (LCGs). A BSR istriggered if data becomes available in an LCG and all other LCGs have nodata, or if data belonging to a logical channel with a higher prioritythan all other LCGs becomes available, or if there is room in the MACProtocol Data Unit (PDU) to send a BSR instead of padding. There may betwo timers which upon expiry trigger BSR. A BSR contains information onthe amount of data available per logical channel group. The BSR iscarried as a MAC control element (CE) in a MAC PDU. Like the SR, thedesign of the BSR for NR may be impacted by the multi-numerology/TTIduration configuration supported in NR. The systems and methodsdescribed herein provide mechanisms for BSR for NR. Uplink scheduling isa key functionality to meet a broad range of use cases includingenhanced mobile broadband, massive MTC, critical MTC, and additionalrequirements. Buffer Status Reports (BSRs) on the other hand carry moredetailed information compared to SR. A BSR indicates buffer size foreach LCG. However, the BSR requires a grant for transmission so it maytake a longer time until the gNB receives it since it may need to bepreceded by an SR. The framework with SR/BSR from LTE may be improved.In an approach, the SR/BSR scheme from LTE can be reused in NR as abaseline. NR should support a wide spread of use cases which havedifferent requirements. In some use cases (e.g., critical MTC andURLLC), NR has tighter latency requirements than has been considered forLTE so far. Also, services such as eMBB can enjoy the enhancements to SRand BSR. In NR, modifications of SR/BSR aim to report the UE bufferstatus (e.g., priority and the buffer size) as well as wantednumerology/TTI duration within the given time constraints. It is assumedthat a mapping of logical channel (LCH) to LCG to numerology/TTIduration will make it possible to infer which numerology/TTI duration touse given the LCG. Hence no explicit signaling of numerology/TTIduration is needed in the SR/BSR if an LCG (or LCH) is present in theSR/BSR. Considering the limitations identified above, it is possible toeither enhance SR with more information bits to indicate moreinformation or enhance BSR.

URLLC provides 1 ms end-to-end radio link latency and guaranteed minimumreliability of 99.999%, which are crucial for some URLLC use cases. SomeURLLC uses cases are described herein and how they map to requirementsat a high level. A URLLC terminal (e.g., UE) will get a benefit frompacket duplication. Radio Link Control (RLC) retransmission (ARQ) is notassumed to be used for meeting the strict user plane latencyrequirements of URLLC. A URLLC device MAC entity may be supported bymore than one numerology/TTI durations. The NR design aims to meet theURLLC QoS requirements only after the control plane signaling forsession setup has completed (to eliminate the case that the UE isinitially in idle). Discontinuous reception (DRX) design will notoptimize for URLLC service requirements. For DL, dynamic resourcesharing between URLLC and eMBB is supported by transmitting URLLCscheduled traffic. URLLC transmission may occur in resources scheduledfor ongoing eMBB traffic. Asynchronous and adaptive HARQ is supportedfor URLLC DL. At least an UL transmission scheme without grant issupported for URLLC. Resources may or may not be shared among one ormore users.

In an implementation, mini-slots have the following lengths. At leastabove 6 GHz, mini-slot with length 1 symbol supported. Lengths from 2 toslot length −1 may be supported. It should be noted that some UEs 102targeting certain use cases may not support all mini-slot lengths andall starting positions. Mini-slots can start at any OFDM symbol, atleast above 6 GHz. A mini-slot may contain Demodulation RS(s) (DM-RS) atposition(s) relative to the start of the mini-slot.

A wide range of URLLC use cases may be supported by NR. 5G aims tosupport a broad range of use cases (or services) and enableground-breaking performance of the URLLC devices (e.g., robots, smartcars, etc.). Some URLLC applications are discussed herein.

One URLLC use case is robotics. 5G needs to improve the response timefor diagnostic situations. For instance, in the near future, robots willbe very low-cost, since robots will only carry around a set of sensors,cameras, actuators and mobility control units. All the intelligentcomputation system, requiring expensive hardware, may be remotely run onan edge cloud.

The sensors and cameras on the robots may be used to monitor theenvironment and capture the data in real time. The captured data will beimmediately transmitted to a central system in a few milliseconds. Thecenter processes the data in an intelligent way (e.g., based on machinelearning and AI (artificial intelligent) algorithms) and makes decisionsfor the robots. The decision/commands may be delivered to the robot veryquickly and the robots will follow the instructions.

The targeted maximum round trip time for this kind of robotic scenariois 1 ms. This may include starting with capturing data, transmitting thedata to the center, progressing data on the center and sending thecommand to the robot, and running the received command.

Another URLLC use case is industrial automation. Industrial automation(together with MTC) is one of the key applications that are consideredwithin 5G systems. Current industrial control systems rely on fast andreliable wired links. However, there exists a large interest inutilizing flexible wireless systems provided by 5G in the future.

This use case considers a combined indoor factory environment, where anumber of objects (e.g., robots, self-driving heavy machines, etc.)perform various dedicated tasks as parts of a production process. Allthese objects are controlled by a production center. These kinds ofindustrial applications require a guaranteed reliability, higher datarate and minimum end-to-end latency within various control processes.

Another URLLC use case is remote surgery and health care. Remote surgerycan be considered as another 5G URLLC use case. With a sense of touch,5G can enable a surgeon to diagnose (e.g., identify cancerous tissue)where the specialist and the patient physically are not able to bepresent in the same room/environment.

In this 5G medical use case, there may be a robotic end which in realtime will provide the sense of touch to the surgeon during a minimallyinvasive surgery. The sense of touch will be captured at the robotic endand, with a latency of few milliseconds, the sensed data will bereflected to the surgeon who is at the other end and wears hapticgloves. On top of that, the surgeon needs to be able to remotely controlthe robotic end as well in a visualized environment. In the remotesurgery scenario, the e2e latency is ideally in the order of severalmilliseconds.

Another URLLC use case is interactive augmented-virtual reality. Ahigh-resolution augmented-virtual reality system is an efficient way todisplay a real or manipulated environment in three-dimensions foreducational purposes, for instance. In one scenario, a number oftrainees are connected in a virtualized real environment/systemsimulator, where the trainees are able to jointly/collaborativelyinteract with each other by perceiving the same environment and the sameartificial subjects and objects. Since the scenario requires interactionbetween the trainees in real time, the targeted round-trip time fromtrainee to the simulator and from simulator back to the trainee shouldbe in the order of milliseconds and not exceed human perception time.

Another URLLC use case is smart vehicles, transport and infrastructure.Self-Driving vehicles can be interpreted as automated driving wherevehicle-to-infrastructure (e.g., smart bus stop, smart traffic lights,etc.) and vehicle-to-vehicle real-time communication is required. Allthese communications can be coordinated in real time by a centralizedsystem (e.g., Intelligent Traffic Management Center (ITMC)).

In such a scenario, the ITMC aims to estimate hazardous conditions wellin advance and decrease the risk of traffic accidents. As an example, asan intelligent system, the ITMC can monitor attributes of the objects inthe traffic based on the object's received data. By doing that, fatalsituations will be anticipated and the system will interact directly(e.g., steer vehicles) even before the drivers to prevent accidents. Inthis kind of traffic scenario, round-trip latencies from vehicles toITMC and ITMC to the vehicles in the order of milliseconds will increasethe traffic safety.

Another URLLC use case is drones and aircraft communication. Drones aregetting increasingly important, especially in the surveillance, publicsafety and media domain. All of these domains come under the criticalcommunication with strict requirements on latency and reliability. Themotivation for such requirements varies from mission criticality tomonetary benefits (e.g., coverage of sports events using drones leadingto in-demand content with high copyrights cost).

Latency and reliability are key factors to control the drones given thenature of use cases considered. Similarly, aircraft communication isalso being considered using NR which also demands the highest standardof reliability and strict latency requirements. The long distances andmobility aspects together with latency and reliability requirementspresent challenges in this use case.

As observed by these use cases, in some URLLC scenarios, mobility is akey requirement together with latency and reliability. A core need ofeach URLLC use case is reliability and latency and these needs shouldhave precedence over resource efficiency due to criticality of thescenarios.

Both International Telecommunication Union (ITU) and 3GPP have defined aset of requirements for 5G, including URLLC. For URLLC reliability, therequirement is the same, whereas for URLLC latency, 3GPP places astricter requirement of 0.5 ms one-way end-to-end latency in UL and DL,compared to 1 ms in ITU.

3GPP has agreed on the following relevant requirements. Reliability canbe evaluated by the success probability of transmitting X bytes within acertain delay, which is the time it takes to deliver a small data packetfrom the radio protocol layer 2/3 SDU ingress point to the radioprotocol layer 2/3 SDU egress point of the radio interface, at a certainchannel quality (e.g., coverage-edge). A general URLLC reliabilityrequirement for one transmission of a packet is 1-105 for 32 bytes witha user plane latency of 1 ms.

User plane (UP) latency can be described as the time it takes tosuccessfully deliver an application layer packet/message from the radioprotocol layer 2/3 SDU ingress point to the radio protocol layer 2/3 SDUegress point via the radio interface in both uplink and downlinkdirections, where neither device nor base station reception isrestricted by DRX. For URLLC, the target for user plane latency shouldbe 0.5 ms for UL, and 0.5 ms for DL. Furthermore, if possible, thelatency should also be low enough to support the use of the nextgeneration access technologies as a wireless transport technology thatcan be used within the next generation access architecture. The valueabove should be considered an average value and does not have anassociated high reliability requirement.

According to IMT 2020, LTE Rel-15 should be able to separately fulfilllow latency and reliability requirements. Low latency may be defined asthe one-way time it takes to successfully deliver an application layerpacket/message from the radio protocol layer 2/3 SDU ingress point tothe radio protocol layer 2/3 SDU egress point of the radio interface ineither uplink or downlink in the network for a given service in unloadedconditions, assuming the mobile station is in the active state. In IMT2020, the minimum requirements for user plane latency is 1 ms for URLLC.

Reliability may be defined as the success probability of transmitting alayer 2/3 packet within a required maximum time, which is the time ittakes to deliver a small data packet from the radio protocol layer 2/3SDU ingress point to the radio protocol layer 2/3 SDU egress point ofthe radio interface at a certain channel quality (e.g., coverage-edge).This requirement is defined for the purpose of evaluation in the relatedURLLC test environment.

The minimum requirement for the reliability is 1-10-5 successprobability of transmitting a data packet of size (e.g., 20 bytes) byteswithin 1 ms in channel quality of coverage edge for the Urbanmacro-URLLC test environment.

Apart from the ITU and 3GPP requirements, there are other interestingcombinations of latency and reliability that may apply to future usecases. One such case is a wide-area scenario with a more relaxed latencybut with high reliability. Therefore, we argue that a network should beable to configure a wide range of latency-reliability settings. Toenable this, several different technological components may beconsidered for URLLC. Therefore, URLLC may fulfil IMT 2020 requirementsand also a wider range of requirements relevant for future use cases.

As mentioned above, a wide range of performance requirements calls for aset of tools for the network to apply according to use case andscenario. At the physical layer, this can include enhanced coding,diversity, repetitions, and extra robust control and feedback. At higherlayers, the focus is fast and reliable scheduling, data duplication, andmobility robustness.

Diversity is a key to achieve high reliability. Whereas one singletransmission (including control message) can be robust (e.g., low BLER),it requires a very low code rate and therefore wide allocations to reachthe target. With diversity, the transmission is spread out in time,space, and frequency, exploiting variations in the channel to maximizethe signal.

In time domain, at least two main options may be employed. One option isthat the transmission is extended over more OFDM symbols and thereby thecode rate is reduced. Alternatively, the transmission is repeated. Arepetition can be automatic (bundled transmissions), or a retransmissiontriggered by feedback.

In frequency domain, the transmission of control and data may berepeated on multiple carriers to exploit frequency diversity of thechannel Frequency repetition of data can be done on lower layers (e.g.,MAC) or in higher layers (e.g., PDCP). Another possibility for achievingfrequency diversity is to spread out parts of the transmissions over awider bandwidth.

For UL transmissions, the basic access may be based on a schedulingrequest (SR). The SR may be followed by an UL grant, and only afterreceiving this grant can the UE transmit UL data. The two firsttransmissions (SR and grant) cause an extra delay, which may be an issuefor delay sensitive traffic. Latency reduction is a feature in LTE-14 toscale down the minimum schedulable time unit so that the absolute timeduration of the first two transmissions is scaled down proportionally.Similar principles can be applied to 5G with tools such as highernumerology. This, in principle, can satisfy the latency requirements andallow several HARQ retransmissions round-trip-time that further enhancethe reliability. However, with higher numerology, it poses challenges tosupport wide-area deployment with power-limited UEs 102 and requires alarger bandwidth. Last but not the least, additional works to enhancereliability for SR and UL grant are required.

As an alternative, the UL grant can be configured (e.g., like SPS UL)with skip padding in LTE. This may be referred to as “Fast UL.” WithFast UL, the UE has a configured UL grant that it may use when it has ULdata. In this setup, the UL latency is similar to that of DL, making itan important enhancement for URLLC.

Given the large BW allocations expected for URLLC UL traffic, aconfigured grant where the gNB 160 pre-allocates a part of the band to aUE can lead to UL capacity problems. This leads to even larger resourcewaste if the URLLC UL traffic is less frequent and sporadic. This issuecan be solved if the same time-frequency resource can be given tomultiple UEs 102.

Collisions may occur in contention-based access. To satisfy the strictURLLC requirements, resolutions must be resolved in a reliable way andremedial solutions may be in place in the event of the collisions. As abaseline, reliable UE identification should be available forcontention-based access in the case of collided transmissions. Afterdetecting the collision, fast switching to grant-based resources shouldbe available. In addition, automatic repetitions with a pre-definedhopping pattern can reduce requirements on collision probability and UEidentification detection.

The requirement on latency and reliability is not only for static UEs102, but also for UEs 102 with different mobility levels for differentuse cases.

Increased robustness can be achieved at higher layers by transmittingduplicates of the data in either the spatial domain (e.g., DualConnectivity), frequency domain (e.g., Carrier Aggregation), or in timedomain with MAC/RLC layer duplication. Optionally, without duplication,better reception quality can be achieved by properly selecting between aset of available connecting links (e.g., Multiple Connectivity).

In another aspect, a buffer status reporting (BSR) procedure may be usedto provide the serving eNB 160 with information about the amount of dataavailable for transmission in the UL buffers associated with the MACentity. RRC controls BSR reporting by configuring the three timersperiodicBSR-Timer, retxBSR-Timer and logicalChannelSR-ProhibitTimer andby, for each logical channel, optionally signaling logical ChannelGroup, which allocates the logical channel to a Logical Channel Group(LCG).

For the Buffer Status reporting procedure, the MAC entity may considerradio bearers that are not suspended and may consider radio bearers thatare suspended. For narrowband Internet of Things (NB-IoT), the Long BSRis not supported and all logical channels belong to one LCG.

A (BSR) may be triggered if any of the following events occur. A BSR maybe triggered if UL data, for a logical channel which belongs to a LCG,becomes available for transmission in the RLC entity or in the PDCPentity and either the data belongs to a logical channel with higherpriority than the priorities of the logical channels which belong to anyLCG and for which data is already available for transmission, or thereis no data available for transmission for any of the logical channelswhich belong to a LCG. In this case, the BSR may be referred to as a“Regular BSR.”

A BSR may also be triggered if UL resources are allocated and the numberof padding bits is equal to or larger than the size of the BSR MACcontrol element plus its sub header. In this case, the BSR may bereferred to as a “Padding BSR.”

A BSR may also be triggered if the retxBSR-Timer expires and the MACentity has data available for transmission for any of the logicalchannels which belong to a LCG. In this case, the BSR may be referred toas a “Regular BSR.”

A BSR may also be triggered if a periodicBSR-Timer expires. In thiscase, the BSR may be referred to as a “Periodic BSR.”

For a Regular BSR, if the BSR is triggered due to data becomingavailable for transmission for a logical channel for whichlogicalChannelSR-ProhibitTimer is configured by upper layers, a UE maystart or restart the logicalChannelSR-ProhibitTimer. Otherwise, ifrunning, the UE may stop the logicalChannelSR-ProhibitTimer.

For Regular and Periodic BSR, if more than one LCG has data availablefor transmission in the TTI where the BSR is transmitted, the UE mayreport a Long BSR. Otherwise, the UE may report a Short BSR.

For a Padding BSR, if the number of padding bits is equal to or largerthan the size of the Short BSR plus its sub header but smaller than thesize of the Long BSR plus its sub header and if more than one LCG hasdata available for transmission in the TTI where the BSR is transmitted,the UE may report a truncated BSR of the LCG with the highest prioritylogical channel with data available for transmission. Otherwise, the UEmay report a Short BSR. If the number of padding bits is equal to orlarger than the size of the Long BSR plus its subheader, the UE mayreport a long BSR.

If the BSR procedure determines that at least one BSR has been triggeredand not cancelled and if the MAC entity has UL resources allocated fornew transmission for this TTI, then the UE may instruct the Multiplexingand Assembly procedure to generate the BSR MAC control element(s). TheUE may start or restart the periodicBSR-Timer except when all thegenerated BSRs are Truncated BSRs. The UE may start or restart aretxBSR-Timer.

If a Regular BSR has been triggered and logicalChannelSR-ProhibitTimeris not running, and if an uplink grant is not configured or the RegularBSR was not triggered due to data becoming available for transmissionfor a logical channel for which logical channel SR masking(logicalChannelSR-Mask) is setup by upper layers, then a SchedulingRequest may be triggered.

A MAC PDU may contain at most one MAC BSR control element, even whenmultiple events trigger a BSR by the time a BSR can be transmitted inwhich case the Regular BSR and the Periodic BSR have precedence over thepadding BSR. The MAC entity shall restart retxBSR-Timer upon indicationof a grant for transmission of new data on any UL-SCH.

All triggered BSRs may be cancelled in case the UL grant(s) in this TTIcan accommodate all pending data available for transmission but is notsufficient to additionally accommodate the BSR MAC control element plusits subheader. All triggered BSRs may be cancelled when a BSR isincluded in a MAC PDU for transmission.

The MAC entity may transmit at most one Regular/Periodic BSR in a TTI.If the MAC entity is requested to transmit multiple MAC PDUs in a TTI,it may include a padding BSR in any of the MAC PDUs which do not containa Regular/Periodic BSR.

All BSRs transmitted in a TTI may reflect the buffer status after allMAC PDUs have been built for this TTI. Each LCG may report at the mostone buffer status value per TTI and this value may be reported in allBSRs reporting buffer status for this LCG.

It should be noted that padding BSR is not allowed to cancel a triggeredRegular/Periodic BSR, except for NB-IoT. A Padding BSR is triggered fora specific MAC PDU only and the trigger may be cancelled when this MACPDU has been built.

A MAC PDU is a bit string that is byte aligned (i.e., multiple of 8bits) in length. As described herein, bit strings are represented bytables in which the most significant bit is the leftmost bit of thefirst line of the table, the least significant bit is the rightmost biton the last line of the table, and more generally the bit string is tobe read from left to right and then in the reading order of the lines.The bit order of each parameter field within a MAC PDU is representedwith the first and most significant bit in the leftmost bit and the lastand least significant bit in the rightmost bit.

MAC SDUs are bit strings that are byte-aligned (i.e., multiple of 8bits) in length. An SDU is included into a MAC PDU from the first bitonward. The MAC entity may ignore the value of Reserved bits in downlinkMAC PDUs.

A MAC PDU includes a MAC header, zero or more MAC Service Data Units(MAC SDU), zero, or more MAC control elements, and optionally padding,as illustrated in FIG. 4. Both the MAC header and the MAC SDUs may be ofvariable sizes. A MAC PDU header may include one or more MAC PDUsubheaders. Each subheader may correspond to either a MAC SDU, a MACcontrol element or padding. Examples of MAC PDU subheaders are describedin connection with FIG. 5.

A MAC PDU subheader may include the five or six header fieldsR/F2/E/LCID/(F)/L but for the last subheader in the MAC PDU and forfixed sized MAC control elements. The last subheader in the MAC PDU andsubheaders for fixed sized MAC control elements may include the fourheader fields R/F2/E/LCID. A MAC PDU subheader corresponding to paddingincludes the four header fields R/F2/E/LCID.

MAC PDU subheaders may have the same order as the corresponding MACSDUs, MAC control elements and padding. MAC control elements may beplaced before any MAC SDU. Padding may occur at the end of the MAC PDU,except when single-byte or two-byte padding is required. Padding mayhave any value and the MAC entity may ignore it. When padding isperformed at the end of the MAC PDU, zero or more padding bytes areallowed. When single-byte or two-byte padding is required, one or twoMAC PDU subheaders corresponding to padding are placed at the beginningof the MAC PDU before any other MAC PDU subheader. A maximum of one MACPDU can be transmitted per Transport Block (TB) per MAC entity. Amaximum of one MCH MAC PDU can be transmitted per TTI.

In the system of FIG. 1A-1D, multiple-input, multiple-output antennasystems coordinate two or four antennas at a time to simultaneously senddata over the same radio channel, increasing data speeds. A phone mighthave a 4×2 MIMO system with 4 receiving (downloading) antennas and 2transmitting (uploading) antennas, with up to an 8×8 array for 5G. Toaddress multiple customers at once, new cell towers will include“massive” 128-antenna arrays with 64 receiving and 64 transmittingantennas. In one embodiment, each antenna of the phone and the celltower is individually steerable. The steering can be done usingindividual motor/actuator, or can be done as a small group of 2×2antennas on the cell tower that communicate with a particular phone. Agroup of antennas can be coordinated to beam at each other. This can bedone using neural network or machine learning to provide real time beamsteering. Moreover, the antennas support carrier aggregation thatenables a radio to increase data capacity. Known as “channel bonding,”5G supports aggregation of up to 16 channels at once, including mixes ofseparate 4G and 5G frequencies. As used herein, beam sweeping may bebroadly defined to include steering, pointing, or directing an antennabeam, electronically and/or mechanically, to provide coverage to adesired region in a service area. Beam sweeping may be commonly appliedto both transmission and reception beams, or separate controls may beapplied independently to the transmission beam and reception beam. Asused herein, the transmission beam is the antenna beam used to providegain and directivity to the transmission signal, and the reception beamis the antenna beam used to provide gain and directivity to the receivedsignal. In an embodiment, a network control device within the networkmay control and coordinate beam sweeping across radio nodes and/or UEdevices. A traffic distribution pattern may be characterized by trafficdistribution data which represents, for example, the amount of dataflowing through network elements and/or network branches interconnectingnetwork elements. The traffic distribution data may include a timehistory of the amount of data, the location of the point of interestwhere the data stream is flowing, the types of data in the data stream,etc.

FIG. 1E shows an exemplary 5G millimeter wave frame structure. As shownDL refers to downlink transmission from eNB to UEs and UL refers touplink transmission from UEs to eNB. As shown control and data planesare separate, which helps in achieving lesser latency requirements. Thisis due to the fact that processing of control and data parts can run inparallel. The mm wave has small antenna and hence large number ofantennas are packed in small size. This leads to use of massive MIMO ineNB/AP to enhance the capacity. Dynamic beamforming is employed andhence it mitigates higher path loss at mm wave frequencies. 5Gmillimeter wave networks support multi-gigabit backhaul up to 400 metersand cellular access up to 200-300 meters. Hover, 5G millimeter wave goesthrough different losses such as penetration, rain attenuation etc. Thislimits distance coverage requirement of mm wave in 5G based cellularmobile deployment. Moreover path loss at mm is proportional to square ofthe frequency. It supports 2 meters in indoors and about 200-300 metersin outdoors based on channel conditions and AP/eNB height above theground. It supports only LOS (Line of Sight) propagation and foliageloss is significant at such mm wave frequencies. Power consumption ishigher at millimeter wave due to more number of RF modules due to morenumber of antennas. To avoid this drawback, hybrid architecture whichhas fewer RF chains than number of antennas need to be used at thereceiver. Moreover low power analog processing circuits are designed inmm wave hardware.

Between bands 30 Ghz and 300 Ghz, mmWave promises high-bandwidthpoint-to-point communications at speeds up to 10 Gbps. But the signalsare easily blocked by rain or absorbed by oxygen, which is one reasonwhy it only works at short ranges. Beamforming is a way to harness themmWave spectrum by directly targeting a beam at a device that is in lineof sight of a base-station. But that means antennas in devices, andbase-stations on network infrastructure, have to be designed to handlethe complexity of aiming a beam at a target in a crowded cellularenvironment with plenty of obstructions.

FIGS. 1E and 1F depict a cell phone that has an RF part including RFTransceiver chip, baseband part comprising of DSP and CPU forcontrolling the data/control messages. ADC/DAC chips are used forinterfacing both RF and baseband parts. The other basic cell phonecomponents include touchscreen display, battery, RAM, ROM, RF antenna,MIC, Speaker, camera, diplexer, micro-USB, SIM slots and others. FIG. 1Fshows an exemplary 5G cell phone architecture. As shown the architectureinclude baseband part, digital RF interface such as DigRF, ADC/DAC andRF Transceiver. The basic components are same in the 5G phone exceptantenna array is used instead of one antenna to support massive MIMO andbeamforming. Quadplexer is used instead of diplexer to support multiplebands. Quadplexer or Quadruplexer is used to multiplex and demultiplexfour radio frequencies to/from single coaxial cable as shown. This helpsin reducing cost and weight as well as uses very smaller area of thephone. This shown 5G cell phone architecture supports millimeter wavefrequency bands. In order to support massive MIMO/beamforming multiplePAs, LNAs, phase shifters, RF filters and SPDT switches are incorporatedin the 5G cell phone design. The 5G phone is backward compatible to2G/3G/4G, WLAN, Bluetooth, GNSS etc. The 5G phone shown is based onheterodyne architecture and advantages of Heterodye receiver. RadioFrequency Front End (RFFE) control signals are used to carry transmittersignal strength indicator (TSSI) and receiver signal strength indicator(RSSI) signals. The temperature control of the beamforming module andits calibration are performed. PMUs (Power Management Units) and LDOs(low drop-out regulators) are used in beamforming part of the 5G cellphone. They transform DC voltage of coaxial cable to different powersupplies for use in various dies for cell phone operation.

The RF frontend transceiver can realize the beam scanning functionthrough a plurality of antenna elements, T/R switches, power amplifierin the transmitter, low noise amplifiers in the receiver, low noiseswitches, phase shifters, and RF signals. The transceiver switches andthe low loss switches can control whether the antenna elements in thesystem receive RF signals or transmit RF signals. When the RF signalsare controlled to be transmitted, the RF signals have different phaseinformation for each link through the phase shifters, and then the RFsignals are amplified by the power amplifiers, which consists of apre-power amplifier and a power amplifier, and finally RF signals aretransmitted to the antenna elements. With different phases of theantenna elements, antenna array can form different beam directions, sothat an optimum beam pointing can be achieved in real time.

Since numerous antennas need to be provided on the mobile device, anantenna system applied in the metal back cover of the 5G mobileterminal, which includes a metal back cover, a signal feeder line, and aplurality of antenna elements. Preferably 3D printing to create acapacitive coupled patch antenna array capable of providing high gainand 360-degree coverage in the elevation plane. A material with arelative dielectric constant 2.2 and loss tangent 0.0009 at thefrequency band of 24-28 GHz is used as the substrate for printed circuitboard (PCB). The patches are printed at the top layer of the substrate.The bottom layer of the substrate consists of the ground plane. Theinner conductor of the coaxial probe feed extends from the ground planethrough the PCB substrate to reach the top layer feed which capacitivelycouples the patches. The antenna element covers 24-28 GHz, which is apossible frequency band for future 5G applications. Four sub-arrays of12 antenna elements, each providing 90 degrees in the elevation plane,were integrated into the mobile phone chassis for 360-degree coverage.The antenna array achieved a high gain of 16.5 dBi in the boresight andcan be steered from −60° to 60° in the phi plane. The physical size ofthe antenna is relatively small compared to existing designs, meaningthat it consumes less space and more antenna elements can be arrangedalong the width of the mobile phone ground plane. The bandwidth of theantenna is sufficient for 5G applications and can be further widened bymodifying the antenna structure.

Turning now to 5G cell towers, a 5G tower is different than a 4G towerboth physically and functionally: more are needed to cover the sameamount of space, they're smaller, and they transmit data on an entirelydifferent part of the radio spectrum. Small cells support high frequencymillimeter waves, which have limited range. The antennas within thesmall cell are highly directional and use what's called beamforming todirect attention to very specific areas around the tower. These devicescan also quickly adjust power usage based on the current load. The smallcell antenna needs to be installed with minimal disruption to localpeople—no street works or construction—and without changing the look ofthe area. They are connected using optical fiber high speed convergednetwork, which also supports other mobile technologies, home broadband,Internet of Things (IoT) and business services. The housing of themobile equipment can be done within street furniture such as manholecovers, lamp-posts and phone boxes to increase the speed and extend thecoverage of a mobile signal along busy roads, town squares and inshopping and entertainment areas. For example, the manhole coverantennae can be installed with minimal disruption to local people—nostreet works or construction—and without changing the look of the area,as the kit is below ground. By connecting the street furniture to 5Gnetwork, the fiber-connected 5G-enabled small antennae are thefoundation on which connected smart cities will be built. 5Gconnectivity will allow connected traffic lights instantly to rerouteroad traffic around congestion, councils automatically to schedulerepairs for broken infrastructure like street lighting, and businessesto manage how much energy they use intelligently.

The 5G ecosystem is expected to support high-density networks by addingnew features to the radios and to the overall system layout. Thetraditional combination in 3G/4G networks of a remote radio headconnected to an external antenna will be extended by active antennasystems (AAS) or active phased-array antennas with massive antennaelements (massive APAA's), in which the electronics will be embedded inthe antenna system and operating over a wide frequency range (600 MHz to28 GHz and above) GHz. This primary system will be supported bycomplementary systems in dense areas with a high number of antennas tosupport multi-user MIMO (MU-MIMO). These antenna elements will featuretheir own control electronics, requiring new connectivity solutions.Frequencies above 6 GHz will be predominately supported by highlyintegrated systems. These radio frequency integrated circuits (RFIC) canfeature integrated antennas on the top surface of the chipset.

FIG. 2A shows an exemplary light post mounted 5G antenna system mountedon a plurality of light posts 11. The light post 11 can also be atraffic light or street sign or utility pole. Small cells areperiodically placed on the traffic light, street sign, or utility polein a neighborhood. A system 1 with a computing unit 10 in communicationwith 5G antenna and city monitoring units, each monitoring unit arrangedto monitor an operational status of at least one street lighting device11. Hence, a single monitoring unit may be configured to monitor one orseveral lighting devices 11 with respect to operational status. Themonitoring units may e.g. be mounted in (or at or in the vicinity of)the street devices 11. In the present example, the street devices 11 areroad lamps arranged to illuminate a road 15 but may alternatively be anyother kind of street devices, such as traffic enforcements cameras ortraffic lights. The computing unit 10 may be in communication with auser interface 19 and a database 18 (or memory or any other means) forstoring region description data. The region description data may e.g. bea region map (such as a road map or geographical map) and/or dataindicative of industrial areas, parks, museums parking lots, averagenumber of people in the region or any other information which may beutilized to prioritize regions e.g. with respect to maintenance urgency.The region description data may be presented e.g. in a map and/or atable over a region in which the street devices 11 are located.

The city/traffic light post cellular device can communicate with acellular device belonging to a person who is crossing a street near thecity light or street light. This is confirmed with camera detection ofthe person crossing the street and if confirmed, the cellular deviceemits a person to vehicle (P2V) or a vehicle to person (V2P) safetymessage to oncoming vehicles to avoid a collision. This system can helpelderly users cross the street safely. The quick speed of the 5G networkenables cars, bikes, and moving vehicles to stop quickly to protect theperson in an emergency where the person is crossing the street withoutadvanced notice to others.

In another embodiment, the camera can detect a pedestrian or personwalking and facing a crossing point. The system sends a confirmation tothe person's cell phone indicating whether the person desires to crossthe street. Once confirmed the system can look up oncoming traffic todetermine a gap in traffic to allow the user to cross the street.Alternatively, instead of automated traffic crossing detection using thecamera, a walking person activates a street button or a cell devicepointing to a desired traversal, the person waits for an indication tocross the street, the system can identify a gap in traffic and signalvehicles behind the gap to stop at the intersection and allow the userto traverse the desired path. After the person safely reaches the otherside of the street, the system can signal vehicles to move again. Thecameras can capture scenarios including: vehicle going straight, vehicleturning right, vehicle turning left, pedestrian crossing, pedestrian inthe road, and pedestrian walking adjacent to the road. The vehicle goingstraight and the pedestrian crossing scenario is the most frequentpre-crash scenario and has the highest cost. The vehicle turning (rightor left) scenarios result in less severe injuries, V2P systemsfunctioning correctly within these scenarios would help maximize crashavoidance. The vehicle going straight and pedestrian either in road oradjacent to the road is lower in occurrence but these crashes tend toresult in fatalities.

In addition to pedestrian assistance, the 5G vehicle communication andcamera combination can handle the following patterns as well:

Intersection Movement Assist (IMA) warns drivers when it's unsafe toenter an intersection due to high collision probability with othervehicles at intersections. The street cameras capture locationinformation from the “cross traffic” vehicle enables the vehicleattempting to cross the intersection to avoid danger, even if the viewis blocked.

Electronic Emergency Brake Light (EEBL) enables a vehicle to broadcast aself-generated emergency brake event to surrounding vehicles. Uponreceiving information from the cameras, the processor determines therelevance of the event and, if appropriate, provides a warning to thecars/drivers, helping to prevent a crash.

Forward Collision Warning (FCW) warns drivers of an impending rear-endcollision with another vehicle ahead in traffic, in the same lane andmoving in the same direction. The camera, along with data received fromother vehicles, determines if a forward collision is imminent and towarn drivers to avoid rear-end vehicle collisions.

Blind Spot Warning (BSW) and Lange Change Warning (LCW) warn driversduring a lane change attempt if the blind-spot zone into which thevehicle intends to switch is, or will soon be, occupied by anothervehicle traveling in the same direction. This is detected by the camerain conjunction with data from vehicles, and the processor sends anadvisory message to the car/driver indicating a vehicle in the blindspot zone. When attempting to merge into the same lane as theconflicting vehicle, the processor sends a warning to the car/driver.

Do Not Pass Warning (DNPW) warns drivers during a passing maneuverattempt when a slower-moving vehicle ahead cannot be passed safely usinga passing zone, because the passing zone is occupied by vehicles movingin the opposite direction. A vehicle sends out an indication on the V2Vit will pass, and the camera captures data and sends advisoryinformation that the passing zone is occupied when a vehicle is aheadand in the same lane, even if a passing maneuver is not being attempted.

Left Turn Assist (LTA) warns drivers during a left turn attempt when itis not safe to enter an intersection or continue in the left turnattempt, due to a car approaching the same path with no intent ofstopping. The camera and processor can provide collision warninginformation to the vehicle operational systems, which may performactions to reduce the likelihood of crashes at intersections and leftturns.

Each monitoring unit may be configured to continuously and/or atpredetermined time intervals and/or upon request (e.g. from thecomputing unit 10) measure (or check) the operational status of thestreet device 11. The operational status may e.g. be indicated byparameters such as light output, energy consumption or any otherparameter relating to the operational condition of the street device 11.Further, the operational status of the street device 11 may be indicatedby a failure signal. The monitoring units may be configured toautomatically transmit the failure indication signal in case the streetdevice is (or is soon) out of function. Further, the monitoring unitsmay be configured to store or measure the geographical positions of thestreet devices 11. For example, a monitoring unit (or the streetdevices) may comprise a GPS receiver for obtaining a GPS position of thestreet device 11.

The monitoring units may communicate (directly or indirectly) with thecomputing unit 10, preferably in an automatic manner. For example, themonitoring units may communicate with the computing unit 10 by means ofradio (or any wireless) communication and/or wired communication such aselectrical/optical communication (e.g. via Ethernet). The monitoringunits may communicate via other units (e.g. servers), which in turncommunicates with the computing unit. Hence, the computing unit 10 mayobtain information indicative of the operational statuses and positionsof the street devices 11 from a peripheral server, which has gatheredsuch information e.g. from the monitoring units.

FIG. 2B shows a block diagram of the unit 11. While the unit can includeconventional yellow sodium vapor lights, white light emitting diode(LED) light is preferred with an adaptive control system to provideenergy efficient lighting. Smart LED streetlights enable the city tomonitor energy consumption and provide the opportunity to dim lightinglevels during late evenings. The unit 11 includes an electronic nose todetect air pollution level. The electronic nose can simply be a MEMSdevice acting as a particle counter. Alternatively, the electronic nosecan detect composition of gas and provide a more detailed report, forexample identifying air pollution as gun power smell, illegal drugsubstance smell, car exhaust smell, industrial pollutant, or rottingmammal smell and such information can be relayed to suitable trashremoval contractors. The unit 11 also includes a microphone array thatcan detect sound and direction of sound. This is useful to detectinggunshots, and the direction of the sound can be triangulated to pinpointthe position of the shooting. The unit 11 also includes a camera, whichcan be a 360 degree camera. Alternatively, the camera can be a 3D camerasuch as the Kinect camera or the Intel RealSense camera for ease ofgenerating 3D models and for detecting distance of objects. To reduceimage processing load, each camera has a high performance GPU to performlocal processing, and the processed images, sound, and odor data areuploaded to a cloud storage for subsequent analysis.

The IoT device can run code to minimize light pollution by lighting onlywith a moving person or vehicle in proximity to the light source. Thisis done by detecting motion near each light pole, and turning on only afew lights in the area of motion while keeping the other lights off.This approach has the advantage of shining light on those who hide inthe darkness for nefarious purposes. The IoT device can run code todetect water pipe rupture by recognizing the position of a fire hydrantand when water motion is detected at the hydrant, the IoT device can runcode to report a fire or emergency to a fire department. The IoT devicecan run code to gate off traffic to the fire or emergency. The IoTdevice can run code to detect car accident and request assistance frompolice or ambulance by detecting car collisions or detecting unusualprolonged traffic at a spot. The IoT device can run code to detect crimeusing a combination of video and sound. The IoT device can run code todiscover anomalies with a particular city block. The IoT device can runcode for providing sensor data to a crowd and requesting from the crowdas a game one or more reasons explaining sensor data.

The device can run code to detect sound direction of sound such asgunshot or gang fight or a crime in progress. Because each light pole issequential, the microphone arrays have high resolution and a combinationof microphone data from an array of light poles on both sides of astreet or freeway provides valuable information in detecting sources ofsound, much like SONAR systems.

On each lighting device 11 is a massive MIMO antenna detailed in FIG. 2Chidden into street furniture such as manhole covers, light poles, andreal/fake trees or plants, or even utility poles. As shown therein, thecombined camera, light, sensor, and massive MIMO antenna unit 11 ismounted on a pole which is secured to the traffic pole cross bar viamounts. For example, a fake tree can be used with solar cells on the topof the leaves and the antenna 11 on the top/bottom of the leaves. Theantenna can be near the top of the manhole cover.

Referring to FIG. 2C, the street lamp includes one or more sensors 13(including microphone), a light source 14, a light pervious cover 15, acamera module 16, and a lamppost. The light source 14 include aplurality of LEDs (light emitting diodes). It is understood that thelight source 14 can also be incandescent lamps and fluorescent lamps.The light pervious cover is light-permeable. The light beams emittedfrom the light source 14 are transmitted through the light perviouscover 15 to illuminate the street. FIG. 2D shows a mounted system 12that does not have light source 14, but has camera 16 and antennas 11.

The antennas 11 can be part of a 5G manhole cover. The manhole coverantennae can be installed with minimal disruption to local people—nostreet works or construction—and without changing the look of the area,as the kit is below ground.

The antenna in unit 11 can also work with traditional cell towerantennas, as shown in FIG. 2F. Among other components not shown, theenvironment 100 generally includes a network 102, a base station 104communicatively coupled to a communications tower 106, and anadministrator's computing device 108. The environment 100 might alsoinclude a technician 110 and a mobile device 109. The components of theenvironment 100 may communicate with each other via the network 102,which may include, without limitation, one or more local area networks(LANs), wide area networks (WANs), and any available networkingconfiguration useable to communicate between networked computingdevices. The network might also include telecommunications networks likea public-switched telephone network (PSTN), 2G/3G/4G/5G, Global Systemfor Mobile Communications (GSM), code division multiple access (CDMA),time division multiple access (TDMA), Wi-Fi, Worldwide Interoperabilityfor Microwave Access (WiMAX), or the like. The network may includeprivate or proprietary networks as well as public networks. Suchnetworking environments are commonplace in telecommunicationsindustries, offices, enterprise-wide computer networks, intranets, andthe Internet. A number of administrator computing devices 108, mobiledevices 109, and user devices (not shown), among others, may be employedwithin the environment 100 within the scope of embodiments of theinvention. Each may comprise a single or multiple devices cooperating ina distributed environment. The administrator's computing device 108 andthe mobile device 109 include any computing devices available in the artsuch as a for example a laptop computer, desktop computer, personal dataassistant (PDA) mobile device, or the like. The computing device 108 andthe mobile device 109 include one or more processors, memories, busses,input/output devices, and the like as known in the art. Further detailof components and internal functionality of the computing device 108 orthe mobile device 109 is not necessary for understanding embodiments ofthe invention, and as such, is not described herein. The computingdevice 108 is communicatively coupled to the network while the mobiledevice 109 may be communicatively coupled to the network and/or may becoupled directly, either wirelessly or through a hardwire connection, tothe tower 106. In an embodiment, a plurality of computing devices 108and/or mobile devices 109 is included in the network. The base station104 comprises any components useable to receive, handle, transmit,and/or operate on data received via the network 102 or from componentson the communications tower 106. In an embodiment, the base station 104is a base transceiver station. The base station 104 is configured likebase stations known in the art and thus may include or becommunicatively coupled to components such as a home location registry(HLR), a short-message service center (SMSC), a multimedia messageservice center (MMSC), signal processors, routers, control electronics,power sources, and the like. Further detail of components andfunctionalities of the base station 104 in addition to those describedbelow will be understood by one of skill in the art and are thus notdescribed in detail herein. The data received and transmitted by thebase station 104 over the network and via the components on the tower106 includes voice and/or data communications for transmission to, orreceipt from a wireless communications network by methods known in theart. The data might also include control signaling for operation ofcomponents mounted on the tower 106 as described below. The base station104 is communicatively coupled to components mounted on thecommunications tower 106. The tower 106 includes an antenna mount 113with a plurality of antennas 116 mounted thereon for broadcasting voiceor data signals to a plurality of mobile user devices (not shown) orother receiving units. Any configuration of components necessary fortransmitting signals from the base station 104 through the antennahousings 116 with antennas 11 mounted on the tower 106 may be employedin embodiments of the invention. For example, antenna housings 116 withantennas 11 are associated with one or more radio units 118 and controlunits 120 that may be included in the base station 104 or mounted at thebase or top of the tower 106 with the antenna housings 116 with antennas11. One or more cables 122, wires, fiber-optic lines, or othercommunicative couplings extend from the base station to the tower 106and up the tower 106 to the one or more of the radios 118, control units120, antenna housings 116 with antennas 11, or other components disposedon the tower 106. In an embodiment, a wireless transceiver 124 isdisposed on the tower 106 for wireless communication of one or moresignals to/from the base station 104 or to/from the technician's mobiledevice 109 to one or more of the radios 118, control units 120, antennahousings 116 with antennas 11, or other components mounted on the tower106. In an embodiment, the base station 104 might include a transmitter228 that provides such wireless communications with the transceiver 124.

The tower 106 can comprise any available tower structure known in theart, such as, for example and not limitation, a mast, a tower, a steellattice structure, a concrete reinforced tower, a guyed structure, acantilevered structure, or the like. Or the tower 106 might compriseother structures like a church steeple, a geologic structure, abuilding, or other structure cable of supporting the antenna mount 113of embodiments of the invention described herein.

The antenna mount 113 can be a ring or generally circular structure 126mounted on the tower 106. The ring structure 126 can be mounted at thetop or at any point along the length of the tower 106 and substantiallyencircles the tower 106. One or more spokes 128 extend radially outwardfrom the tower 106 to the ring structure 126 and couple the ringstructure 126 to the tower 106. One or more of the spokes 128 includes apassageway 130 interior to the spoke 128 and traversing the length ofthe spoke 128. The passageway 130 is configured to receive cables 132,wires, fiber optic strands, or other communications components therein.The ring structure 126 is generally circular in shape but may compriseany form or shape that substantially encircles the tower 106. In anembodiment the ring structure 126 only encircles a portion of the tower106. The ring structure 126 has a generally C-shaped cross section thatforms a channel 134 disposed therein that is open to the environmentgenerally along the perimeter of the ring structure 126. The channel 134extends into a body 136 of the ring structure 126.

While housing 116 is rectangular in shape, it can be spherical, balloonshape, semispherical, parabolic, inverse parabolic, pyramidal, amongothers. A spherical dielectric lens can provide a multi-beam, high gainantenna system for fifth generation (5G) wireless communications. Thelens is ideally of the Luneburg type lens. To approximate the focusingproperties of the Luneburg lens in a manner that is practical forfabrication purposes, monolithic lenses can be used where the lens iscomprised of a single, homogeneous dielectric material, layered lenseswhere the lens is formed of spherical shells of homogeneous material,and lenses formed by additive or subtractive manufacturing methods wherethe lens dielectric constant is synthesized by voids formed in otherwisesolid dielectric materials. The shells could be connected in anysuitable manner, such as by being bonded together on their touchingsurfaces, or they could be bolted together with non-metallic fasteners.

Objects that have the same shape as each other are said to be similar.If they also have the same scale as each other, they are said to becongruent. Many two-dimensional geometric shapes can be defined by a setof points or vertices and lines connecting the points in a closed chain,as well as the resulting interior points. Such shapes are calledpolygons and include triangles, squares, and pentagons. Other shapes maybe bounded by curves such as the circle or the ellipse. Manythree-dimensional geometric shapes can be defined by a set of vertices,lines connecting the vertices, and two-dimensional faces enclosed bythose lines, as well as the resulting interior points. Such shapes arecalled polyhedrons and include cubes as well as pyramids such astetrahedrons. Other three-dimensional shapes may be bounded by curvedsurfaces, such as the ellipsoid and the sphere. A shape is said to beconvex if all of the points on a line segment between any two of itspoints are also part of the shape. The housing 116 can have any of theseshapes.

Another embodiment uses an active antenna architecture with combinedantenna/radio head with distributed radio functionality across antennaelements. The term fronthaul is used to describe the connection betweenthe cell tower radio itself and the mobile network control backbone (theBaseband Unit or BBU) and CPRI is a well-known standard for thisinterconnection. Backhaul is the linkage between a base station and thecore wired network, and is often fiber or coax, and in some casesbroadband, proprietary wireless links. Fronthaul, backhaul, and varioushybrid architectures will be needed to accommodate cost efficient,backwards compatible, dense deployment of network infrastructurenecessary to provide the broadband, low latency demands for 5G systems.In one embodiment, a remote fronthaul access point is placed in thecenter of the triangle and communicates with the radio head in theactive antenna via fiber optics or ultrawideband radios.

Another embodiment fuses fronthaul and backhaul into an integrated 5GTransport Network as a flexible, reconfigurable, software definedtransport architecture. A single network is used support a variety offunctional splits between the antenna and the packet core. This alignswith the evolution of Network Function Virtualization (NFV) and CloudRAN (CRAN) which points to the neural network plane or data center thatcan be configured to support whatever functional split is deployed inthe network. At one extreme, a legacy base station and backhaul can beaccommodated. At the other extreme, a network of densely distributedradio heads configured for massive MIMO can exchange compresseddigitized radio samples for cloud-based processing. 5G-Crosshaul, aEuropean 5GPPP project, can act as a bus/transport network connectingRadio Heads to BBUs which will be virtualized. Once virtualized, basestation functions can be flexibly distributed and moved across datacenters, providing another degree of freedom for load balancing.

Near the tower can be mounted a baseband unit cabinet. The baseband inthe cabinet has a fiber optic output connection using the common publicradio interface (CPRI) protocol and small form factor pluggable (SFP)connectors to fiber. The baseband also has a power output 216 to deliverpower for the active antenna. CPRI fiber extends up the pole or mast tothe active antennas. The antennas are arranged in the figure as fourantennas for each of three sectors. In the active antenna, the radiohead takes the output of the CPRI interface, which is digital, turns itinto an analog radio frequency signal, amplifies it through a PA anddrives the 5G antenna.

Wireless radios may be integrated into the antennas for short-distanceinter-antenna communication. The radios may operate at a high frequency,such as millimeter-wave or 60 GHz, and may be ultrawideband UWB radios.At high frequencies such as used by these radios, high data rates arepossible, sufficient to handle the digital data demands for digitalfronthaul traffic, with minimal interference to the reception andtransmission frequencies of the radios. The wireless range limitationsof frequency bands in the tens of gigahertz (i.e., microwave ormillimeter wave) are not problematic, as the antennas areco-located/mounted on the same radio tower. In some embodiments,backhaul may also be wireless using UWB radios. Backhaul to one antennamay be shared with other antennas, in a mesh network.

A baseband board may be provided to perform all baseband functionsspecific to an antenna. The baseband board may include DPD and CFRfunctions, as well as self-test routines and modules, as well ashandling for one or more channels of MIMO, or one or more channels ofmultiple radio access technologies, e.g., 2G, 3G, 4G, 5G, 6G UMTS, LTE,and the like. At the bottom of the mast, cabinet 421 no longer needs ashelter with air conditioning, as the reduction in power wastage andincrease in thermal mass enables passive cooling at the cabinet.Therefore, no AC and no baseband unit are found at the cabinet; instead,only a passively cooled power supply and a backhaul network terminal areprovided in the cabinet.

In some embodiments, a power tilt antenna chassis may be provided. Insome embodiments, a winch that can lower itself and that causes theantenna to guide itself into position when it is raised can be deployedat the tower in a base or cradle for the antenna module. A drone mayoperate an electric latch to release an antenna module, and the antennamodule may lower itself to the ground using the winch. In someembodiments, a boom and trolley may be attached at the center of a towerfor attaching and detaching antenna modules. The antenna chassis and/orbase may be physically designed to be self-guiding, such that a newantenna may be inserted into the base by a drone or by an operator.

In some embodiments, wireless synchronization may be used betweenantennas. Synchronization is important for various applications, such astime division duplexing (TDD) for certain cellular bands. Directwireless synchronization could be provided or each antenna subsystem maybe equipped with its own GPS antenna, and the GPS antennas may be usedto sync the antennas together down to approximately 50 parts per billion(ppb).

FIG. 2G illustrates a simplified digital baseband beamformingarchitecture that digitally applies complex beamforming weights(composed of both a gain and phase factor) in the baseband domain.Antenna-based communication systems may utilize beamforming in order tocreate steered antenna beams with an antenna array. Beamforming systemsmay adjust the delay and/or gain of each of the signals transmitted by(or received with in the receive direction) the elements of an antennaarray in order to create patterns of constructive and destructiveinference at certain angular directions. Through precise selection ofthe delays and gains of each antenna element, a beamforming architecturemay control the resulting interference pattern in order to realize asteerable “main lobe” that provides high beam gain in a particulardirection. Many beamforming systems may allow for adaptive control ofthe beam pattern through dynamic adjustment of the delay and gainparameters for each antenna element, and accordingly may allow abeamformer to constantly adjust the steering direction of the beam suchas in order to track movement of a transmitter or receiver of interest.

Digital beamformers may employ digital processing in the baseband domainin order to impart the desired phase/delay and gain factors on theantenna array. Accordingly, in digital beamforming systems, the phaseand gain for each antenna element may be applied digitally to eachrespective antenna signal in the baseband domain as a complex weight.The resulting weighted signals may then each be applied to a separateradio frequency (RF) chain, which may each mix the received weightedsignals to radio frequencies and provide the modulated signals to arespective antenna element of the antenna array.

As shown in FIG. 2G, digital beamformer 150 may receive baseband symbols and subsequently apply a complex weight vector pBB=[α1 α2 α3 α4] T tos to generate pBBs, where each element α1, I=1, 2, 3, 4 is a complexweight (comprising a gain factor and phase shift). Accordingly, eachresulting element [α1s α2s α3s α4S] T of pBBS may be baseband symbol smultiplied by some complex weight α1. Digital beamformer 150 may thenmap each element of pBBs to a respective RF chain of RF system 152,which may each perform digital to analog conversion (DAC), radio carriermodulation, and amplification on the received weighted symbols beforeproviding the resulting RF symbols to a respective element of antennaarray 154. Antenna array 154 may then wirelessly transmit each RFsymbol. This exemplary model may also be extended to a multi-layer casewhere a baseband symbol vector s containing multiple baseband symbolss1, s2, etc., in which case baseband precoding vector pBB may beexpanded to a baseband precoding matrix pBB for application to basebandsymbol vector s. In this case, α1, i=1, 2, 3, 4 are row vectors, andpBBs=[α1s α2s α3s α4s] T. Thus, after multiplying pBB and s, the overalldimension is the same as the overall dimension at the output of digitalbeamformer 150. The below descriptions thus refer to digital beamformer150 as pBB and transmit symbol/vector as s for this reason while thismodel can be extended to further dimensions as explained.

By manipulating the beamforming weights of pBB, digital beamformer 150may be able to utilize each of the four antenna elements of antennaarray 154 to produce a steered beam that has a greater beam gaincompared to a single antenna element. The radio signals emitted by eachelement of antenna array 154 may combine to realize a combined waveformthat exhibits a pattern of constructive and destructive interferencethat varies over distances and direction from antenna array 154.Depending on a number of factors (including e g antenna array spacingand alignment, radiation patterns, carrier frequency, etc.), the variouspoints of constructive and destructive interference of the combinedwaveform may create a focused beam lobe that can be “steered” indirection via adjustment of the phase and gain factors α1 of pBB. FIG.2G shows several exemplary steered beams emitted by antenna array 154,which digital beamformer 150 may directly control by adjusting pBB.Although only steerable main lobes are depicted in the simplifiedillustration of FIG. 2G, digital beamformer 150 may be able tocomprehensively “form” the overall beam pattern including nulls andsidelobes through similar adjustment of pBB.

In so-called adaptive beamforming approaches, digital beamformer 150 maydynamically change the beamforming weights in order to adjust thedirection and strength of the main lobe in addition to nulls andsidelobes. Such adaptive approaches may allow digital beamformer 150 tosteer the beam in different directions over time, which may be useful totrack the location of a moving target point (e.g. a moving receiver ortransmitter). In a mobile communication context, digital beamformer 150may identify the location of a target User Equipment (UE) 158 (e.g. thedirection or angle of UE 156 relative to antenna array 154) andsubsequently adjust pBB in order to generate a beam pattern with a mainlobe pointing towards UE 156, thus improving the array gain at UE 156and consequently improving the receiver performance. Through adaptivebeamforming, digital beamformer 150 may be able to dynamically adjust or“steer” the beam pattern as UE 156 moves in order to continuouslyprovide focused transmissions to UE 156 (or conversely focusedreception).

Digital beamformer 150 may be implemented as a microprocessor, andaccordingly may be able to exercise a high degree of control over bothgain and phase adjustments of pBB through digital processing. However,as shown in FIG. 1 for RF system 152 and antenna array 154, digitalbeamforming configurations may require a dedicated RF chain for eachelement of antenna array 154 (where each RF chain performs radioprocessing on a separate weighted symbol α is provided by digitalbeamformer 102); i.e. NRF=N where NRF is the number of RF chains and Nis the number of antenna elements.

Hybrid beamforming solutions may apply beamforming in both the basebandand RF domains, and may utilize a reduced number of RF chains connectedto a number of low-complexity analog RF phase shifters. Each analog RFphase shifter may feed into a respective antenna element of the array,thus creating groups of antenna elements that each correspond to aunique RF phase shifter and collectively correspond to a common RFchain. Such hybrid systems may thus reduce the number of required RFchains by accepting slight performance degradations resulting from thereliance on RF phase shifters instead of digital complex weightingelements.

In one embodiment the digital beam former provides a method ofmitigating interference from interfering signals. The system tracks thelocation of interfering signals and readjusts the digital beam formingcoefficients to create nulls in the antenna pattern directed towardsthat interfering signal. The digital beam forming coefficients areadjusted to improve or maximize the signal quality of communicationsignals received from the UEs. The UE provides the cell tower BS withquality indicators which indicate the quality of the signals received bythe UE. In response to received link quality indicators, the digitalbeam former in the BS dynamically adjusts its antenna directionality andthe antenna beam pattern to help optimize the signal transmitted to theUE. The digital beam forming coefficients are readjusted to continuallyhelp maintain and help improve or maximize the signal quality of thereceived signals as the UE and/or the cell tower change their relativepositions. The digital beam former coefficients are adjusted to providemore antenna beams to geographic regions having high demand forcommunication services and also adjusted to provide fewer antenna beamsto regions having a low demand for communication services. In thepreferred embodiment, as the demand for communication services changeswith respect to geographic location, the digital beam former dynamicallyassigns antenna beams or assigns additional beams in response to thechanges in demand for communication services.

In another embodiment the UE receives a link quality indicator from a BS(or another UE) that it is communicating with. The link qualityindicator (LQI) provides preferably 3 data bits indicating of thequality of the signal received at the BS. This link quality indicator isprovided back to BS or UE which accordingly adjusts its transmit digitalbeam forming coefficients dynamically to improve the quality of itstransmitted signal. In this embodiment a local processor, DSP, or aneural network plane evaluates the link quality indicator and adjuststhe beam forming coefficient provided to transmit digital beam formingnetwork. In general this causes the transmit and receive antenna beamcharacteristics to be more optimized for the particular situation the UEis currently experiencing. The situation includes interferencecharacteristics from other signals, interference characteristics causedby ground terrain and the specific receiver antenna characteristics ofthe receiving base station and/or satellite.

In another embodiment the UE tracks the communication signal from thebase station and cell tower as the UE moves. This tracking is done byone of a variety of ways including using the receive signal andanalyzing the angle or direction of arrival of the receipt signal.Alternatively, as the UE moves, the antenna beams, preferably bothtransmit and receive, are continually adjusted to help improve signalquality. Accordingly, the resulting antenna beam patterns are directedtowards the communication station, while nulls are directed toward anyinterfering signal source. As the UE moves (or the small cell/cell towermoves), the antenna beam characteristics, through the use of the digitalbeam former, are adjusted to maintain improved communication with the BSand preferably remain directed towards the BS as the BS moves relativeto the UE or vice versa.

Digital beam former of FIG. 2G provides for positioning of nulls in theantenna beam pattern and provides for beam shaping and other beamcharacteristics that are dynamically modified through the use of thesedigital beam forming techniques. In a preferred embodiment, the digitalbeam former provides dynamically reconfigurable antenna patterns basedon current traffic demand levels. For example, one antenna beam providesbroad coverage over a large region having a low demand for communicationservices, while other antenna beams are small and provide a highconcentration of communication capacity in a region having high demandfor communication services. In another embodiment, antenna beams areshaped in responsive to demand for communication services. Antenna beamsare modified and shaped, for example, to approximate the contour of ageographic region having high demand for communication services next toan area having virtually no demand for communication services.Accordingly, communication capacity may be concentrated where it isneeded. In the preferred embodiment, antenna beams are dynamicallyconfigured in real time in response to demand for communicationservices. However, in other embodiments of the present invention,antenna beams are provided based on historic and measured demand forcommunication services.

In one embodiment, the UE listens for signals, preferably within thesmall cell's footprint. Preferably, receive beam controller moduleconfigures the antenna beams to provides at least one broad antenna beamcovering substantially an entire small cell footprint. Accordingly,signals are received from anywhere within that footprint on that oneantenna beam. Signals that are received may include signals fromexisting users that are already communicating with the small cellsystem, interfering signals, e.g., signals from non-system usersincluding interfering signals, and signals from system users requestingaccess to the system.

The neural network plane determines whether or not the signal is onefrom an existing user. In general, the location of existing users isknown. If the signal received is not from an existing user, the systemdetermines the location of that signal source. Those of skill in the artwill recognize that various ways may be used to determine the geographiclocation of a signal source. Those ways may include analyzing the angleof arrival, the time of arrival, frequency of arrival, etc.Alternatively, if the signal source is a user requesting system access,that UE may provide geographic coordinates on its system access requestsignal.

Once the location of the signal source is determined the systemdetermines whether or not the signal is an interfering signal. In otherwords, the system determines if the signal source will interfere with aportion of the spectrum assigned to the small cell system, oralternatively, if the interfering signal is a communication channelcurrently in use with a UE communicating with the small cell. If thesystem determines that the signal source is not an interfering signaland that the signal source is a request for a new channel, the systemassigns an antenna beam to that user. The system may employ varioussecurity and access request procedures which are not necessarilyimportant to the present description. Beam control modules then generateindividual receive and transmit antenna beams directed to that UE atthat UE's geographic location. The system preferably, repeatedly adjuststhe DBF transmit and receive coefficients to help provide improvedsignal quality received from the UE.

In one preferred embodiment of the present invention the UE provides alink quality indicator (LQI) that indicates the quality of the receivedsignal. The UE provides that link quality indicator to the small cell.The link quality indicator is evaluated causing transmit beam controlmodule to adjust DBF control coefficients to help optimize thetransmitted antenna beam to the UE.

When the system determines that the signal source is an interferingsignal, for example a non-system user, the system calculates and adjustthe receive DBF coefficients provided to receive DBF network to helpreduce or minimize interference from the interring signal. In oneembodiment of the present invention, the system places a “null” in theantenna pattern in the direction of the interfering signal. Theinterfering signal is continually monitored and tracked as either the UEmoves or the interfering signal moves.

When the system has determined that the signal source is an existinguser, the system determines when a hand-off is required. In someembodiments of the present invention the UE requests hand-offs while inother embodiments, the neural network plane determines when a hand-offis necessary. Preferably, hand-offs are determined based on signalquality. In general, a hand-off is requested when a user is near theedge of the antenna pattern footprint region or exclusion zone.

In one preferred embodiment of the present invention, antenna beams areindividually provided to the UE and the individual antenna beam tracksthe location of the UE. Accordingly, hand-offs are only between smallcells and necessary at the edge of the small cell footprint. When ahand-off is necessary, the system assigns a new antenna beam fromanother small cell to the user. If a hand-off is not required, in-bandinterference is monitored along with received power level and linkquality metrics.

The receive and transmits digital beam former (DBF) coefficients areadjusted to help maintain an improved or maximum signal quality, to helpreduce or minimize in-band interference and to help maximize receivepower level. During this “tracking” mode, additional interfering signalsmay cause a degradation in signal quality. Accordingly, the systemdynamically readjusts the DBF coefficients to help maintain signalquality. In one embodiment of present invention link quality indicatorsare provided by BSs or UEs. Accordingly, the combination provide fortracking of the UE as the relative location between the UE and the smallcell change. The system determines when a hand-off is required. If ahand-off is not required the UE remains in the tracking mode. When thehand-off is required the system will execute a hand-off to the nextsmall cell. In one embodiment of the present invention the next smallcell is notified that a hand-off is required and it is provided thegeographic location of the UE. Accordingly, the next small cell canassign and generate an antenna beam specifically for that UE beforebeing released from its present small cell. Once the UE is handed off tothe next small cell, the system adds the available antenna beam to itsresource pool, allowing that antenna beam to be available to be assignedto another UE.

In another embodiment, the neural network plane determines the locationof high demand and low demand geographic regions and this can beaccomplished in any number of ways. For example, each UE communicatingwith the system has a geographic location associated therewith.Furthermore, each UE requesting access to the system may provide thesystem with geographic location data. Once the geographic locations ofhigh demand and low demand areas are determined, the system causes theDBF beam control modules to provide less antenna beams in low demandareas and provide more antenna beams in high demand areas. In oneembodiment of the present invention, each antenna beam provides alimited amount of communication capacity.

Low demand areas are provided with antenna beams having a much largercoverage region than antenna beams being provided to high demand areas.For example, antenna beam covers a large geographic region thatcurrently has a low demand for communication services. Alternatively,antenna beams have much smaller geographic coverage regions and providemore communication capacity for a region that currently has a highdemand for communication services. In another embodiment of the presentinvention the systems adjust the shape of the antenna beams based on thedemand for communication services. For example, antenna beams can belong narrow beams formed to provide better area coverage forcommunication services.

As the demand for communication services changes, antenna beams aredynamically provided in response. As the day begins, antenna beams areinitially at homes. As the day progresses, the antenna beams transitionto office locations as the time of day changes in response to demand forcommunication services. In the case of a natural disaster where demandfor communication services may be particularly great, dedicated antennabeams may be provided. A small cell control facility may direct smallcell's digital beam former to allocate beams accordingly. In general,antenna beams preferably are provided in response to the changing demandof communication services using the neural network plane without theassistance of operators.

A network element may control beam sweeping of radio nodes and/or userequipment (UE) devices within a service area based upon trafficdistribution data, networking requirements (e.g., such as user servicerequirements and/or application service requirements) and/or prior beamsweeping history. Beam sweeping may be broadly defined to includesteering, pointing, or directing an antenna beam, electronically and/ormechanically, to provide coverage to a desired region in a service area.Beam sweeping may be commonly applied to both transmission and receptionbeams, or separate controls may be applied independently to thetransmission beam and reception beam. As used herein, the transmissionbeam is the antenna beam used to provide gain and directivity to thetransmission signal, and the reception beam is the antenna beam used toprovide gain and directivity to the received signal. In an embodiment, anetwork control device within the network may control and coordinatebeam sweeping across radio nodes and/or UE devices. In an embodiment,the network control device may be a centralized, self-organizing networkengine which has visibility into the traffic distribution patterns ofthe network. As used herein, a traffic distribution pattern may becharacterized by traffic distribution data which represents, forexample, the amount of data flowing through network elements and/ornetwork branches interconnecting network elements. The trafficdistribution data may include a time history of the amount of data, thelocation of the point of interest where the data stream is flowing, thetypes of data in the data stream, etc. The system may determine beamsweeping patterns for antenna beams associated with one or moretransmission reception points (TRP). TRPs may be located at one or moreantennas attached to radio nodes and/or UE devices. Network controldevice may have visibility into traffic distributions and networkrequirements, and may receive various inputs from different sources tocalculate beam sweeping commands based on the received inputs. In anembodiment, inputs received by network control device 120 may includethe traffic distribution data and other networking requirements. Basedon the received inputs, network control device may provide beam sweepingcommands to radio nodes and/or UE devices. The networking requirementsmay include, for example, service requirements associated with one ormore applications on one or more UE devices 160 (also referred to hereinas “application service requirements”) in service area. The applicationservice requirements may include requirements defined by a subscriptionand/or service contract corresponding to a user and the user'sassociated UE (also referred to herein as “user service requirements”).Beam sweeping commands may be provided to individual radio nodes and/orUE devices via core network and mobile backhaul network. Radio nodes mayforward beam sweeping commands to respective UE devices 160 over awireless channel (e.g., a wireless control channel). Additionally,network control device 120 may prioritize particular antenna beams,where high priority beams are reserved to service users having highnetworking requirements. In an embodiment, high priority beams may beclassified as “active” beams. Beams having lower priority than activebeams may be classified as “candidate” beams, which may be selected toreplace active beams if necessary. Beams having lower priority thanactive and candidate beans may be classified as “alternative” beams,which may be used as backup beams in case an active beam is temporarilyblocked and a suitable candidate beam is unavailable. In addition, thepriority of beams may be updated according to the time of day,particular days or dates (e.g., workdays, weekends, holidays, etc.),and/or the time of season (to account for seasonal effects ofpropagation, seasonal variations of the density of users, and/orvariations in objects which may block signal propagation). In addition,network control device 120 may also use prior knowledge of prior beamsweeping patterns to influence the determination of current and/orfuture beam sweeping patterns. Moreover, the beam sweeping patternsassociated with control signaling broadcast between radio nodes and UEdevices may be adjusted differently than antenna beams associated withdata bearing channels. Additionally, differences between beam sweepingpatterns may be based on the beam width of individual antenna beamsand/or the number of beam sweeping positions.

Once the beam sweeping pattern is determined, processor may provide beamsweeping commands to control the beam sweeping pattern for one or moreradio node(s) and/or one or more UE device(s) in the service area. Forexample, for radio node(s), beam sweeping commands may be provided overthe network and received by network interface in radio node(s), and beused by antenna controller to control antenna array. In another example,the beam sweeping commands may be provided over the network and be usedby antenna controller to control antenna array.

The processor can provide commands, to at least one of a radio node orthe UE device in the service area, wherein the provided commands controlthe beam sweeping patterns for at least one of the radio node or the UEdevice. The system can receive performance requirements correspondingwith an application associated with the at least one UE device. Theprocessor can receive, from the core network, service requirementsassociated with the at least one UE device. The determining the beamsweeping pattern for all the beam sweeping positions further includes:ordering the sweeping factors associated with each beam sweepingposition; and determining a priority value of each beam sweepingposition based on the ordering.

In an embodiment, process may iterate over N beam sweeping positions,and in each loop the process includes receiving location data from UEdevice(s) and may identify, at each beam sweeping position, the UEdevice(s) located therein and traffic distribution data associated withUE device(s). The traffic distribution data may define data trafficpatterns to provide quantitative and/or qualitative information ofnetwork data traffic for each UE. The process can also base its beamsweep from performance requirements corresponding with applicationsassociated with UE device(s) or service level requirement or applicationservice requirements. The needs for each UE are summed up to determinesweeping factors and the process may determine the beam sweeping patternby ordering the sweeping factors associated with each beam sweepingposition, and then determining a priority value of each beam sweepingposition based on the ordering. This may be performed by determining therank of the sweeping factors or by classifying each beam sweepingposition. Once the beam sweeping pattern is determined, processor mayprovide beam sweeping commands to control the beam sweeping pattern forone or more radio node(s) and/or one or more UE device(s) and may beused by antenna controller to control antenna array.

FIG. 2H-2I shows an exemplary active antenna system (AAS) and a remoteradio head (RRH) connected to a baseband unit with a high-speed seriallink as defined by the Common Public Radio Interface (CPRI), Open BaseStation Architecture Initiative (OBSAI), or Open Radio Interface (ORI).The high speed serial link is used to transport the Tx and Rx signalsfrom the BBU to the RRH or AAS. In an RRH, the downlink (Tx) signal isdigitally upconverted and amplified on the downlink path.Correspondingly the analog uplink (Rx) signal is processed by a lownoise amplifier (LNA), downconverted and digitized. The duplexed outputsfrom the RRH feed a passive antenna array via a corporate feed networkwith RET support. The RRH comprises two transceivers, one for each MIMOpath. Each transceiver incorporates an upconverter, an amplifier, anLNA, a downconverter, and a duplexer.

In an active antenna, each element in the antenna array is connected toa separate transceiver element. A typical AAS system may therefore havemultiple transceivers (for example 8-16). Since there are many moretransceivers/amplifiers in an AAS, each amplifier in an AAS delivers amuch lower power when compared to an amplifier in an equivalent RRH. Thebenefits of AAS over an RRH based site architecture include: sitefootprint reduction, distribution of radio functions within the antennaresults in built-in redundancy and improved thermal performance, anddistributed transceivers can support a host of advanced electronicbeam-tilt features that can enable improvements in network capacity andcoverage. The integration of the radio within the antenna is theelimination of components like cables, connectors, and mounting hardwareand an overall reduction in the physical tower space required. Byintegrating the remote radio head functionality into the antenna, theaesthetics of the site can be improved and wind load reduced, resultingin lower leasing and installation costs.

The active antenna architecture can eliminate a substantial portion ofthe power losses in the RF feeder cables when compared to a conventionalBTS. Additionally, the active antenna can support an electronic beamtilt without requiring a Remote Electrical Tilt (RET) feeder network.This further reduces the power loss for an AAS when compared to an RRHwith a RET. In most configurations this can increase the power deliveredto the antenna when compared with an RRH. The additional margin can beused to lower the overall thermal dissipation in the amplifiers.

Further, with the radios integrated directly into the antenna housing,and with replacement of a small number of large amplifiers with manysmall amplifiers, the heat is spread over the larger antenna structureas opposed to the smaller RRH or amplifier shelf. This availability ofhigher surface area for heat dissipation results lower temperature risesin the components, which results in improved thermal margins and betterreliability.

The distributed and redundant architecture of the AAS, wherein eachantenna element is fed by its own transceiver, provides reliabilitybenefits as the failure of one transceiver does not cause a criticalfailure. The system is intelligent and can sense a transceiver failure.When a transceiver does fail, the amplitude and phases on the remainingelements are automatically adjusted digitally to compensate for theelevation beam distortion and the reduction of EIRP on the horizon. Withthe appropriate sizing of the amplifiers and intelligent readjustment ofthe amplitudes and phases, the AAS can be designed to have minimal or noloss in coverage performance with a single transceiver failure andminimal degradation with two transceiver failures. Since the likelihoodof more than one transceiver failing in a single AAS is minimal, veryhigh system availabilities can be achieved.

Since the AAS can be designed to have minimal loss in performance with asingle transceiver failure, repairs and site upgrades for failed unitscan be delayed and scheduled. For a site with several sectors and bands,multiple unscheduled repair visits (as would be the case for an RRHbased system) can be replaced by a single scheduled visit that is lessfrequent. This can significantly reduce the operational costs foroperators.

The AAS can electronically tilt elevation beams by having independentbaseband control of the phase, amplitude, and delay of individualcarriers on each antenna element. This supports multi-mode systems wheredifferent carriers in the same frequency band, with different airinterfaces, may require different tilt orientations. The flexibilitywith tilt control in AAS enables advanced RF planning features, much ofwhich can potentially reduce the cost to operators by reducing thenumber of sites required. The electronic tilt capability also allows forthe separate beam tilting and optimization of the Tx (downlink) and Rx(uplink) paths for cases when the link budgets for the Tx and Rx pathsare unequal. It may also be used to optimize cell radii when thephysical layer (modulation scheme) for the Tx and Rx paths is different,as is the case with LTE. Tilt can be adjusted on a per-carrier basis.This can be used vertical sectorization in LTE and RAN sharing for UMTS.In UMTS/LTE networks, adding sectors in the vertical plane can be donewhere the first carrier may cover an inner sector whereas a secondcarrier covers an outer sector.

As multiple operators vie for precious real estate on tower tops,antenna sharing and RAN sharing amongst two or more operators can bedone. The RAN that supports a multicarrier UMTS system is shared by twooperators with each operator controlling/owning one or more of theindividual carriers. Since the RF planning and site deployments arelikely to differ among operators, each UMTS carrier may need to betilted by different amounts in order for each operator to achieveoptimal network performance and optimizing beam tilt on a per-carrierbasis based on active channel loading using Self-organizing networks(SON) algorithms can provide even higher network efficiencies.

FIG. 2J shows an exemplary edge processing system with an example ofEdge Computing Support to Services for Connected and AutonomousVehicles). In this system, vehicles can off load certain autonomousdriving tasks to powerful edge processors (processor, graphicalprocessing units, field programmable gate arrays (FPGA), or specializedlearning/inferencing systems), and to edge sensors (radar, lidar, highresolution camera, V2X sensors). Such use of edge processing with lowlatency allows costs to be shifted from the car or the phone toresources on the 5G network. Battery life also improves due to reducedwork on the car or the phone, for example.

Exemplary edge compute systems and methods are described herein. In oneembodiment, an antenna system includes edge computing cluster withmultiple CPU/GPU on the antenna for the lowest latency. In FIG. 2J, thesystem includes an antenna, an ultrawideband (UWB) transceiver (5Gtransceiver) coupled to the antenna (such as a mm antenna), and acompute array including at least a CPU and a GPU to perform edgeprocessing at the antenna site with data sets that require ultra-lowlatency access. For additional loads that require intermediate lowlatency access, the processing load can be transferred to a computecluster of CPUs/GPUs at a 5G head end. For non-time sensitive largeloads, cloud servers such as those at a remote data center such asAmazon or IBM data center can process the data and return the result tothe phone. Machine learning and analytics are provided at the edgelevel, the core level, and the cloud/data center level.

To illustrate one example, when an application captures a video of ascene that includes a building with video recognition operations, whichmay include breaking a frame of video camera input into an array offeatures and sending those features to the GPUs on the antenna to dofeature recognition and send results identifying the building to theapplication. Alternatively, the system may offload feature recognitionprocessing to an intermediate edge layer, an intermediate core layer, oreven to a cloud/data center layer to do batch processing in the cloud ifthere is no need for instant recognition. While the offloaded featurerecognition processes are being executed at one of the layers maycontinue with other processing, such as rendering, doing a base featureanalysis to identify features in frames, and retrieving virtual objectsfor display to augment captured video camera frames. By intelligentlyand dynamically determining how to assign processing of components andtasks based on predefined factors, the edge processing may adjust howcomponents and tasks are performed on demand and in a manner that mayfacilitate efficient use of resources and/or an improved userexperience. Thus, the system may determine best resources (e.g., bestpaths and best processor slicing per GPU) from task to task on an asneeded basis, instead of being preprogrammed. This may providepotentially unlimited ability to process components and tasks at leastbecause processing of the components and tasks may be divided up acrossthe network in an intelligent and meaningful way that facilitatesefficient use of resources and/or an improved user experience. With lowlatency of data communications between the application and processingcapacity located at the antenna edge of network, specificlatency-sensitive and/or computationally intensive components or tasksof client may be offloaded in a manner not doable previously inconventional data-processing architectures. The offloaded tasks may beexecuted using shared, accelerated microservices at the edge of network,at acceptable latencies due to the 5G speed. In addition, hardware-basedencoding and decoding processes on the application and various layersmay enable high-speed and/or low-latency offloading such as loading datainto flat buffers and streaming the flat buffer data.

In certain examples, the edge processing system may support a virtualreality streaming architecture with end-to-end streaming delivery ofvirtual reality content on demand and/or in real time (e.g., withouthaving to download data for an entire virtual reality world in advanceof rendering). For example, in VR, the cloud layer and the edge layermay receive distinct and matching datasets from a smart phone or anexternal source for processing. The edge layer may process on its copyof the data stream to generate real-time views, and cloud layer mayprocess on its copy of the data stream to generate batch views. Incertain implementations, computing components and/or tasks that may beoffloaded to the edge layer may include, without limitation, distributedrendering, certain components or tasks of distributed rendering such aslight map and/or light field tasks (calculating and providing a lightfield), computer vision such as image and object recognition and/ortracking tasks, state management such as state management of a virtualreality world (e.g., state management of rooms and/or users of a virtualreality world), procedural generation of assets such as virtual realityassets, world caching such as virtual reality world caching, screenspace processing components or tasks such as calculations related todetermining field of view in a virtual reality world, indirect lightingcomponents or tasks such as ray tracing operations, physics processingcomponents or tasks such as fluid dynamics operations, any othersuitable components or tasks of an application executing on clientdevice, or any combination or sub-combination of such components and/ortasks. For example, in an AR application, the camera on the glass/phonecan capture the environment and edge processors on the 5G networkantenna or head end can perform object recognition and sizing and returnsuch info to the glass/phone. When the user selects a prospectfurniture, for example, a 3D rendering of the furniture in theenvironment can be done so that the glass/phone can do AR withoutconsuming significant compute cycle, leading to longer battery life forthe client glass, phone, or device. In another application formaintenance, a user can select a unit to be repaired, and the image ofthe unit is processed by edge processor/GPUs to identify the serviceableunit, and instructions are retrieved from a cloud data center withimages, and the edge processor combines the data to overlay with theviews on the glass/phone to provide step by step repair instructions tobe rendered on the glass, phone, or wearable device to avoid reducingbattery life of the wearable device.

In another VR example, a user controlling an avatar in a first-personvirtual reality experience may direct the avatar to open a door to aroom to reveal a new scene to the avatar. The local edge processors/GPUsmay quickly render lower-quality graphics of the room first and thenscale up the graphics so that the user can see immediately instead ofwaiting for higher-quality graphics to become available. In certainimplementations, the edge or core layers may provide a close-by cache ofgraphics at the edge of network to allow fast rendering of a view at lowquality while higher-quality models are computed and/or retrieved.

In certain implementations, systems and methods described herein may beused to provide a streaming virtual reality service to end uses of theservice. This may include streaming virtual reality data representativeof one or more persistent 3D virtual reality worlds, which streamed datamay be fundamentally different from virtual reality data representativeof other types of conventional virtual reality worlds in various ways.For example, conventional technologies stream virtual reality worlds byvirtualizing hardware and software needed to process and render thevirtual reality worlds. As such, when a conventional service provides astreaming virtual reality world to a user using this virtualizationparadigm, there may be a virtualization of hardware and software of avirtual reality presentation system (e.g., a gaming PC loaded withsoftware representative of the virtual reality world) that exists withinthe cloud (i.e., exists on one or more servers with which a clientdevice used by the user is communicating by way of a network). Thevirtualized system may be dedicated to providing a virtual reality worldto a particular user and may thus correspond to the client device usedby the user in a one-to-one fashion. For example, user inputs may becommunicated from the client device to the cloud-based virtualizationsystem, and a video stream may be communicated from the virtualizationsystem back down to the client device.

As a result, users experiencing a virtual reality world by way of thisconventional paradigm may be assigned such cloud-based virtualizationsof virtual reality presentations systems, and each system may provide arespective user with virtual reality content in essentially the same waythat a system would if it were localized to the user, rather than in thecloud. For example, even though a particular user may receive virtualreality data in a stream (e.g., from the cloud) rather than from a localvirtual reality presentation system (e.g., a gaming PC owned by theparticular user), the software running on the respective virtualizedsystem assigned to the particular user must be compiled, built, anddistributed to be run and used by the user. Consequently, just as withnon-streamed virtual reality data that is processed and renderedlocally, changing aspects of virtual reality data streamed in thisconventional way (e.g., aspects such as the presence, behavior,functionality, appearance, etc., of objects within the virtual realityworld) may require recompilation, rebuilding, and redistribution of thevirtual reality data loaded onto the virtualized system. At the veryleast, a software patch or the like would need to be distributed andinstalled by each virtualized system streaming the conventional virtualreality world.

In contrast, virtual reality data representative of persistent 3Dvirtual reality worlds may operate using a one-to-many stateful paradigmthat is fundamentally different from the one-to-one paradigm describedabove. Rather than loading software representative of the virtualreality world onto dedicated, one-to-one virtualizations of virtualreality presentation systems as described above, a single systemmaintaining a world state of a persistent 3D virtual reality world(e.g., data representative of which objects are within the world, wherethe objects are disposed within the world, how the objects are movingand/or interacting within the world, etc.) may deliver the same stateinformation to many different clients (e.g., client devices of differentusers) that may perform the processing and rendering on their own and/orby offloading certain components or tasks to edge servers relativelyproximate to the clients as latency limitations may allow. Accordingly,virtual reality data representative of a persistent 3D virtual realityworld may be broadcast or multicast to many client devicessimultaneously to allow many users to jointly experience the persistent3D virtual reality world while only one instance of the world ismaintained (e.g., rather than many different instances of the worldbeing maintained on individual virtualized systems, as described above).Additionally, as these persistent 3D virtual reality worlds arecontinuously streamed, all aspects of the appearance and functionalityof the persistent 3D virtual reality worlds and components therein maybe modular, open, dynamic, and modifiable at run time.

As such, one benefit of persistent 3D virtual reality worlds (e.g.,virtual reality worlds streamed using a stateful, one-to-many paradigmsuch as described above) is that 3D virtual reality worlds may bedynamically changed (e.g., on demand, at run time as the virtual realitydata is streaming) in various ways without the virtual reality datarepresentative of persistent 3D virtual reality worlds being recompiled,rebuilt, or even updated by way of a software patch or the like. Forexample, as the virtual reality data of persistent 3D virtual realityworlds is streaming, a provider of the virtual reality data may be ableto freely add or remove objects from the worlds, add games that may beplayed by users, update or adjust the code on which the world is running(e.g., to add new features or functionality, to fix bugs, etc.), changethe functionality of the worlds (e.g., modify the physics of the worlds,etc.), and/or otherwise dynamically contextualize the worlds in anysuitable way.

Another benefit of persistent 3D virtual reality worlds is that virtualreality data of such worlds may be broadcast to many users at oncewithout requiring dedicated virtualizations of hardware and software tobe assigned and reserved for all of the users. In some examples, variousdifferent persistent 3D virtual reality worlds offered by a provider maybe broadcast to a subscriber base of many users as virtual realityexperience options. As such, users in the subscriber base may instantlyswitch from world to world in an analogous way as users may switchthrough different television channels. Like television channels, various3D virtual reality worlds may persistently exist and be persistentlyprovided to users as options to experience regardless of which worlds(if any) the users actually select and experience. Also analogous totelevision channel surfing, client devices used to instantly switch fromworld to world in this way may not store or receive any data related tothe different persistent 3D virtual reality worlds prior to presentingthe worlds to respective users. All the data needed to process, render,and present a particular persistent 3D virtual reality world to the usermay be provided at run time when the world is selected.

By offloading compute intensive tasks to the 5G network, the result is ahigh-performance system that is inexpensive yet has low powerconsumption. For example, in AR/VR application, the 3D rendering can bedone at the antenna or head end so that light weight, fashionableglasses can be worn; in autonomous application, non-critical imageprocessing and learning can be offloaded to the edge computers. Exampleof such automotive applications include the generation of HD maps,in-cab entertainment, non-invasive health monitoring (drugs,distraction, drowsiness, . . . ) among others.

In one embodiment, smart vehicles share the expensive peripherals suchas lidars, radars, high resolution cameras, or people/obstacle sensors,which are located at the 5G antennas for rapid local response. It isexpected that roll out of 5G would initially be in metropolitan areas,so such sharing results in low vehicular cost as such vehicles would useinexpensive radar and cameras. However, when the smart vehicle leaves 5Gcoverage area, such resources may not be accessible. In such case, thevehicles would only need inexpensive radars and cameras, with HD mapsand inter-vehicle communication for self-navigation.

In another embodiment, a virtual reality/augmented system transfers thegraphics processing to the edge node to either 1) save power consumptionif the GPU is in the phone, or 2) avoid the cost of a high-end GPU inthe phone.

In another embodiment, learning systems on the phone may need to beupdated with new training data. Such updates can be offloaded to the GPUon the antenna, and the neural network weights on the learning machinerunning on the phone can be updated.

In another embodiment, for additional processing need, the CPU/GPUcluster can be located at the head. Such system provides low-latencydata communications over a network and utilizes the low-latency datacommunications and network-based computing resources to optimizeperformance of computing tasks of an application executing on a clientdevice, particularly performance of computing tasks that arecomputationally intensive and/or latency sensitive. For example, systemsand methods described herein may leverage low-latency datacommunications and a distributed data-processing architecture to offloadperformance of select computing tasks of the application to one or moreedge computing devices while performing other select computing tasks ofthe application at the client device. This may allow offloading ofcertain latency-sensitive and/or computationally intensive tasks thatpreviously could not be offloaded from the client device due tolatencies and configurations of conventional data-processingarchitectures.

The Edge Computing system provides application and content providerswith cloud computing capabilities and IT service environment at the veryedge of the mobile network. This environment is characterized by theproximity, often in both physical and logical sense, to the clients,enabling very low latency between the client and the serverapplications, high bandwidth for the application traffic, and nearreal-time access of the applications to context-rich information, e.g.related to device locations and local radio network conditions. Edgecomputing thus ensures high quality of experience with highlycontextualized service experience and efficient utilization of radio andnetwork resources. The edge processing enables cars, AR/VR units,surround video systems, or other compute intensive devices to betterhandle the big volume of data while the 5G network can dynamicallyallocate CPU and Acceleration resources based on the services' needs(e.g., computer vision Vs. video streaming Vs. data aggregation).

Network Slicing can tailor the capacity and capabilities of the networkfor each different service. Service-specific profiles can be used fordynamic assignment of service-specific HW-acceleration to optimize thecompute and storage based on simultaneous services requirements. Networkslicing may be implemented based on cloud technologies, SDN, NFV,network orchestration, OpenFlow, etc., and network slices may beconfigured to meet specific applications, services, and end devicesdemands. For example, a network slice may be expected to meet certainreliability, latency, bandwidth, and/or other quality-of-service (QoS)requirement.

First, network resource and capability information pertaining to anetwork may be stored and a request for a network service, from a user,may be received. Network level requirement information, which supportsthe network service, may be generated based on the request. Availablenetwork resources, which satisfy the network requirement levelrequirement information, may be selected based on the network resourceand capability information. The system may select network resources(e.g., communication links, network devices, a RAN, a core network, adata center, etc.) that satisfy the prescriptive information and areavailable. A cost pertaining to the selected available network resourcesmay be calculated, for example the cost can be indexed to a networkresource, a group of network resources, and/or an aggregate ofend-to-end network resources, as previously described, based on theselected available network resources.

A network slice deployment layout information may be generated based onthe selected network resources and the calculation. For example,depending on the network service requested, the network service may bedeployment in a centralized manner or in a distributed manner. Dependingon the number of locations of end users relative to the destinationnetwork device that provides the network service, transport costs maydiffer between a centralized-based network slice delivery layoutcompared to a distributed-based network slice delivery layout.Additionally, for example, for each type of layout (e.g., centralized,distributed, etc.), there may be multiple networks, routing within anetwork, data centers to use, and/or other network resources from whichto select. The system may determine an optimal usage of networkresources based on the cost information from an end-to-end network sideperspective, a network perspective, and/or a network resourceperspective. The candidate network slice deployment layout informationmay include virtual network function descriptor information, networkservice descriptor information, and network slice descriptorinformation. Next, the network slice deployment layout may beprovisioned based on the generation. For example, the system candetermined the candidate network slice deployment layout information toprovision the network service based on the network slice deploymentlayout information.

The network infrastructure for connected autonomous driving (AD)vehicles will provide not only Network as a Service (NaaS) but alsoInfrastructure as a Service (IaaS) in a multi-tenancy fashion. The edgecomputing infrastructure represents a pool of connected resources thatcan be used by the Mobile Network Operators (MNOs) and also by diverseservice/application providers who commercialize services as well asmicro-services (e.g., Augmented Reality to serve multiple services,Video Analytics to serve diverse services) for connected AD vehicles.The information exchanged between vehicles, infrastructure, pedestrians,and network using V2X technology enables a multitude of new and excitingapplications.

Edge computing deployment can be used for Real-Time SituationalAwareness & High Definition (Local) Maps use case due to the real-timeand local nature of the information needed for accurate and augmentedsituational awareness of the road users. An alternative to edgecomputing is to make the road users themselves create and maintain theirreal-time situational awareness from the broadcast information theyreceive from peer users. Edge computing solution allows offloading suchtasks to the network edge, by augmenting the broadcast information withother available information via data fusion from available sources, andefficiently broadcast large amounts of data to many users locally.

Advanced Driving Assistance can use the edge processing. In Real-TimeSituational Awareness & High Definition Maps use, the system can alertdriver of Host Vehicle (HV) moving forward of hazard (icy) roadconditions in front. In Cooperative Lane Change (CLC) of AutomatedVehicles Driver of Host Vehicle (HV) signals an intention to change thelane with at least one Remote Vehicle (RV) in the target lane in thevicinity of the HV. In See-Through use cases (part of a class ofapplications called High Definition Sensor Sharing), vehicles sharecamera images of road conditions ahead of them to vehicles behind them.The See-Through (For Passing) application is an example of an AdvancedDriving Assistance application. It warns the driver of a host vehiclewho is intending to pass a remote vehicle of potential hazards such asan oncoming remote vehicle in the passing lane or a lack of room infront of the leading remote vehicle. The goal of the use case is toprevent catastrophic head-on collisions during a passing maneuver. Othercases include Safety (intersection assist), Convenience (softwareupdate), and Vulnerable Road User (VRU). The VRU use case coverspedestrians and cyclists. A critical requirement to allow efficient useof information provided by VRUs is the accuracy of the positioninginformation provided by these traffic participants. Additional means touse available information for better and reliable accuracy is crucial toallow a real-world usage of information shared by VRUs. The VRUs makingtheir presence/location known through their mobile devices (e.g.,smartphone, tablets), along with vehicle's use of that information, canimprove traffic safety and to avoid accidents.

FIG. 2K shows exemplary vehicles that can be used to supplement 5Gservices as mobile 5G cell towers. For example, drone arrays can be setup to beam signals to client devices and carry 5G active antennas toallow the BS to communicate with the UEs. On the air,balloons/planes/helicopters, and even LEOs can be used to provide radiocommunications to the client devices with the 5G active antennas toallow the BS to communicate with the UEs. On the ground,trucks/buses/vans/cars can provide mobile 5G radio support. Such groundvehicles can use elevatable antenna that extends to increase height tothe 5G active antennas to allow the BS to communicate with the UEs.

In one embodiment, a hybrid lighter than air/heavier than aircraft orair vehicle can be used as a Geostationary balloon satellites (GBS) areatmosphere analogues to satellites at a fixed point over the Earth'ssurface and the GBS can carry 5G active antennas to allow the BS tocommunicate with the UEs. In one embodiment, the lighter than air gascan be helium to ascend, and an airbag that compresses air to allow thedrone to descend. Alternatively air can be liquified using ultra lowtemperature refrigeration such as LN2 cryogenic refrigeration. Solarcells provide energy for the GBS, and the hybrid air propulsion systemspends about half of its time as heavier than air and half of its timeas lighter than air vehicle to provide propulsion using variablebuoyancy propulsion to allow the balloon to move into and maintain itsposition with minimal power consumption. In another embodiment, inaddition to solar panels the GBS can receive laser power from a grounddevice that the GBS hovers over. Antennas would be auto-steered to aimdirectly at UEs they communicate with. In yet another GBS embodiment, anautonomous variable density, variable volume articulated aircraft has anaircraft body including a section defining a contractible and expandableaircraft body in cross section, a storage tank fixed to the aircraftbody, a mass of first gas having a density less than air within one ofthe chambers, a medium for readily absorbing large masses of the firstgas within the storage tank to appreciably reduce the volume of the onechamber carrying the gas, the amount of the first gas and the absorbingmedium being sufficient to permit a change in density of the aircraftfrom lighter than air to heavier than air and vice versa, a pump totransporting the first gas from the one chamber to the tank absorptionthereof, and a pump for selectively driving the gas from the absorbingwithin the tank to the one chamber for increasing the volume of gaswithin the compartment and the size of the aircraft body and reductionin density of the aircraft. In one embodiment, the medium for absorbingthe first gas comprises water, the aircraft further comprising conduitfluid connecting the one chamber to the storage tank, pump providedwithin the conduit means for pumping gas from the one chamber to thestorage tank and the conduit terminating in a gas diffuser within thetank submerged within the water. One embodiment drives the gas from theabsorbing medium with a heater operatively positioned with respect tothe tank for heating the solution formed by the water absorbing thefirst gas to release the gas from the liquid.

In another aspect, a drone can be used to supply the GBS with power. Inone embodiment, the drone can swap battery with the GBS. In thisembodiment, the GBS has a plurality of energy sources including at leastone battery port or chamber having a latch to secure the battery to theGBS. A drone brings up a battery unit near the battery port of thedrone, unlatches or unscrews the depleted battery and stores thedepleted battery into a chamber. Lowering the battery can disconnect oneor more couplings. One or more other disconnects can be used in someimplementations. For example, separate quick disconnects can be used forrespective high-voltage connection, low-voltage connection and a coolantconnection. When the battery is successfully mounted onto the GBS, anyquick disconnects on the GBS are then properly connected withcorresponding disconnects on the new battery pack. This can ensureproper connection of high voltage, low voltage and liquid coolant to theGBS. For example, the GBS's internal system can check whether there isany water intrusion into the battery pack, or whether there are anyshort circuits. A replacement battery is then positioned in the exposedbattery chamber, and arms on the drone secure the latch to seal thebattery chamber. The refueling drone can detach from the GBS and goes tothe next battery to be swapped on the GBS, and if done, the drone canreturn to a home station.

In another embodiment, the GBS is powered by hydrogen fuel cells, andthe drone can refuel the GBS with gas or hydrogen fuel. Prior to flyingto the GBS to refuel it, the internal hydrogen storage tanks in therefueling drone must be filled. A hydrogen storage subsystem is providedwithin the transportable hydrogen refueling station to refill or chargethe lightweight composite hydrogen storage tanks, a quick connect, whichcan be any standard hydrogen connector, is used to connect an externalhydrogen source to hydrogen storage subsystem. Downstream from the quickconnect is a pressure release valve. The pressure release valve is asafety element to prevent hydrogen, at a pressure exceeding apre-determined maximum, from entering the hydrogen storage subsystem. Ifthe pressure of hydrogen being introduced through the quick connectexceeds a safe limit a restricted orifice working in combination with apressure relief valve causes the excess hydrogen to be vented through avent stack. In general, the valves are used to affect the flow ofhydrogen within the refueling station. A check valve, between the ventstack and pressure relief valve, maintains a one way flow of the flow ofpressurized hydrogen being relived from the storage subsystem. Therestrictive orifice also prevents the hydrogen from entering thepressure rated feed line at a rate which causes extreme rapid filling ofthe lightweight hydrogen storage tanks. Prior to connecting the quickconnect nitrogen gas, or other inert gas can be introduced into the feedline to purge any air from the feed line. Pressurized nitrogen dispensedfrom a nitrogen tank can be introduced through a nitrogen filling valve.One or more hydrogen leak sensors are also distributed and connected tothe system controller. The pressure of the gaseous hydrogen is measuredby one or more pressure sensors placed in the feed line. The firstcompressor subsystem contains an oil cooled first intensifier. An oil toair heat exchanger for cooling hydraulic oil which is supplied to afirst intensifier heat exchanger to cool the first intensifier. Theintensifier is a device, which unlike a simple compressor, can receivegas at varying pressures and provide an output stream at a near constantpressure. However, it may be suitable in some cases to use a compressorin place of an intensifier. The pressure of gaseous hydrogen whichenters a second compressor subsystem at about 4,000 psi can be increasedto achieve the desired 10,000 psi. The system controller can be used tomaintain balance during the refilling of the lightweight compositehydrogen storage tanks by monitoring the pressure of each of thelightweight composite hydrogen storage tanks via adjacent pressuresensors. The system controller, in turn can switch between storage tanksand select which tank to fill at a given time interval during thefilling.

The refueling drone can be used for refueling from the high pressuretanks. The hydrogen fueling subsystem is used to refuel an externalhydrogen storage vessel in the GBS with pressurized hydrogen from therefueling drone. As the refueling begins after the system controllerwill check pre-identified parameters, such as, temperature and pressureof the external hydrogen storage vessel, confirmation of groundconnection and in some cases, confirmation from vehicles of readiness tofill, in order to determine whether hydrogen should be dispensed to theexternal hydrogen vessel. The actual hydrogen refueling process can bepreceded by safety measures. Pressurized nitrogen, or other inert gas,may be introduced through a purge line into the hydrogen dispensing feedlines to purge any air from the hydrogen dispensing feed lines. Thepurge is to manage the risk of dangerous hydrogen-air (oxygen) mixturesbeing formed and or being supplied to the external hydrogen vessel.Purge pressure relief valves are appropriately located to vent gas fromthe hydrogen dispensing feed lines. One proposed industry standard for afuel cell vehicle fill coupler is described in the proposed “FuelingInterface Specification” prepared by the California Fuel CellPartnership that description which is hereby incorporated by reference.The fill coupler, indicated in the proposed “Fueling InterfaceSpecification”, has a “smart” connect which, among other parameters,checks the pressure, temperature and volume of hydrogen within the tanksof a vehicle 12 (the external hydrogen storage vessel 25) beingrefueled. It will also check that the vehicle is grounded. The “smart”fill coupler can communicate with the refueling drone and after theexternal hydrogen vessel and the fill coupler are connected, rechargingor filling of the hydrogen receptacle can occur. When refueling orrecharging an external hydrogen storage vessel preferably a map of theexternal hydrogen vessel should be obtained. A map should check thetemperature, volume and pressure of the hydrogen gas in the externalhydrogen vessel and the volume pressure and temperature of the hydrogenin each lightweight composite hydrogen storage tanks and the map mayinclude information about the pressure rating and capacity of theexternal hydrogen vessel. By controlling the temperature of the hydrogengas during refueling a faster refueling can take place. If thetemperature of the hydrogen in the external hydrogen vessel increasepast ambient the volume of hydrogen which the external hydrogen vesselcan store is decreased. Temperature management supports fasterdispensing of dense gaseous hydrogen.

Preferably, the refueling drone designed for boom-type transfers inwhich a boom controller extends and maneuvers a boom to establish aconnection to transfer hydrogen fuel from the refueling drone to therefueling drone. Prior to refueling, the refueling drone extends arefueling probe. The refueling probe, when fully extended, may be longenough for the refueling drone to safely approach and connect to therefueling probe. The distal end of the refueling probe connects to areceptacle 108 on an exterior of the refueling drone.

The refueling drone needs to be able to maneuver into position foraerial refueling and maintain its position during the refueling. Therefueling drone includes a navigation system that may be used forpositioning the refueling drone during aerial refueling. The GBSnavigation system provides inertial and Global Positioning System (GPS)measurement data to the refueling drone via a data link. The navigationsystem then uses the inertial and GPS data for both the refueling droneand the GBS to compute a relative navigation solution, otherwisereferred to as a relative vector. Preferably, the relative navigationsolution is a GPS Real-Time Kinematic (RTK)/INS tightly coupled relativenavigation solution. The relative navigation solution is calculatedbased on what data is available to the navigation system and allows theGBS to accurately and confidently maintain its relative position to therefueling drone. The navigation system includes an Inertial NavigationSystem (INS), a GPS, a navigation processor, and a system processor. Thenavigation system may have other sensors, such as magnetometer, an airdata computer, and antennas for the data link and the GPS sensors. TheINS may provide acceleration and angular rate data for the refuelingdrone. The refueling drone may include a similar INS for generatinginertial data to be transmitted to the refueling drone. Typically, theINS relies on three orthogonally mounted acceleration sensors and threenominally orthogonally mounted inertial angular rate sensors, which canprovide three-axis acceleration and angular rate measurement signals.For example, the INS may include three accelerometers and threegyroscopes. The three accelerometers and three gyroscopes may bepackaged together with a processor, associated navigation software, andinertial electronics. The inertial electronics may be used to convertthe acceleration and angular rate data into a digital representation ofthe data. The type of relative navigation solution provided by thesystem processor depends on the type of data available to the systemprocessor. The relative position may be a simple difference of theplatform (i.e., the GBS and the refueling drone) reported PVTs of auniquely derived integrated relative GPS/INS solution. The types ofplatform navigation solutions include a GPS-only solution, a looselycoupled GPS/INS solution, and a tightly coupled GPS/INS solution thatincorporates any combination of the other solutions. In addition to theplatform PVT solutions, measurement data from both platforms may alsoavailable and be used to compute the relative solution independently ofthe PVT solutions being provided by each platform. It is important tonote that the relative navigation solution is not limited to thesesolutions. For example, the relative navigation solution may also be anultra-tightly coupled solution. The relative vector is calculated usingthe available data and processing techniques. A fixed solution ispossible when a double difference (DD) process is able to confidentlyresolve the carrier phase DD integer ambiguities. A float solution isavailable when there exists five or more common sets (i.e., common tothe GBS and the refueling drone) of GPS pseudorange and carrier phase.Relative GPS (RGPS) refers to a GPS-based relative solution that doesnot take into account the inertial measurement data from either the GBSor the refueling drone. Coupled or blended solutions integrate theavailable data (both GPS and INS) to form a relative vector between theGBS and the refueling drone. Depending on the distance between therefueling drone and the GBS, and the data link message content, therefueling drone selects the best available solution for relativenavigation. The required level of performance, in terms of accuracy andintegrity, is a function of the level of safety required for navigation.In general, the closer the GBS is to the refueling drone, the moreaccurate the relative navigation solution should be to avoid anunanticipated collision, while maintaining the refueling position. Theprotection levels associated with the relative vector are a function ofthe type of measurements available for processing and the confidence inthose measurements from the GBS to the refueling drone. The protectionlevels associated with the relative vector may also be a function of therange from the GBS to the refueling drone. With multiple sets ofmeasurement data, it is possible to calculate several relativenavigation solutions. For example, if the refueling drone has three EGIsystems on board and the GBS has two EGI systems on board, the systemprocessor may form up to thirty independent relative navigationsolutions. The multiple navigation solutions may be compared. If one ormore of the navigation solutions is not consistent with the othernavigation solutions, the system processor 208 may discard theinconsistent relative navigation solutions. In this manner, the failureof a GPS receiver and/or an inertial sensor may be detected and isolatedby the system processor 208. A threshold for identifying inconsistentnavigation solutions may be adjusted based on the requirements of aerialrefueling. Aerial refueling requirements may be set by one or moreregulatory agencies.

In one embodiment, a plurality of relative navigation solutions iscalculated by the system processor. A flock of bird approach may beused. The type of relative navigation solution can vary based on thedata available to the system processor. The number of relativenavigation solutions calculated depends on the number of EGI systems onboard the GBS and the refueling drone, and the currently available datafrom each sensor. Preferably, each of the solutions has the samebaseline (assumes lever arms between EGI systems and accompanying GPSantennas). Next, the relative navigation solutions are compared witheach other. The comparison detects whether any of the relativenavigation solutions is inconsistent with the other solutions. Aninconsistent solution may be an indication that one or more of the GPSreceivers and/or inertial sensors is malfunctioning. The consistencyinformation may be used to form a protection level for the relativenavigation solution. The relative navigation solutions are compared to athreshold, such as the protection level determined by the consistencyinformation. At block 308, if a particular relative navigation solutionexceeds the threshold, the system processor 208 discards the solution.Otherwise, at block 310, the solution is used to navigate the refuelingdrone during aerial refueling. As a result, the refueling drone maysafely and efficiently rendezvous with the GBS for aerial refueling.

In another embodiment, a kite can be tethered to a ground station andcarry 5G active antennas to allow the BS to communicate with the UEs.The kite can carry propeller engines to provide propulsion if needed.The cable tethering the kite to the ground station supplies power andfiber optic broadband communication for the 5G active antennas to allowthe BS to communicate with the UEs.

In another aspect, a moveable vehicle including a pole and a top portionto mount 4G antennas and a 5G housing, wherein the pole is retractableand extendable during 5G operation; and one or more antennas mounted onthe 5G housing and in communication with a predetermined target using 5Gprotocols.

In another aspect, a system includes an airborne frame to mount 4Gantennas and a 5G housing; and one or more antennas mounted on the 5Ghousing and in communication with a predetermined target using 5Gprotocols.

Turning now to the details of the antenna that converts electriccurrents into electromagnetic waves and vice versa, the antenna can beconsidered a complex resistive-inductive-capacitive (RLC) network. Atsome frequencies, it will appear as an inductive reactance, at others asa capacitive reactance. At a specific frequency, both reactance's willbe equal in magnitude, but opposite in influence, and thus cancel eachother. At this specific frequency, the impedance is purely resistive andthe antenna is said to be resonant. The frequency of the electromagneticwaves is related to the wavelength by the well-known equation λ=c/f,where f is the frequency in hertz (Hz), λ is the wavelength in meters(m), and c is the speed of light (2.998×108 meters/second). Sinceresonance will occur at whole number fractions (½, ⅓, ¼, etc.) of thefundamental frequency, shorter antennas can be used to send and recoverthe signal. As with everything in engineering, there is a trade-off.Reducing the antenna's size will have some impact on the efficiency andimpedance of the antenna, which can affect the final performance of thesystem. A half-wave dipole antenna has a length that is one-half of thefundamental wavelength. It is broken into two quarter-wave lengthscalled elements. The elements are set at 180 degrees from each other andfed from the middle. This type of antenna is called a center-fedhalf-wave dipole and shortens the antenna length by half. The half-wavedipole antenna is widely used as it cuts the antenna size in half whileproviding good overall performance. The dipole antenna can have one ofthe quarter-wave elements of a dipole and allow the ground plane on theproduct's pc board to serve as a counterpoise, creating the otherquarter-wave element to reduce size. Since most devices have a circuitboard, using it for half of the antenna is space efficient and can lowercost. Generally, this half of the antenna will be connected to groundand the transmitter or receiver will reference it accordingly. Thisstyle is called a quarter-wave monopole and is among the most commonantenna in today's portable devices. Another way to reduce the size ofthe antenna is to coil the element. This is where the straight wire iscoiled or wrapped around a non-conductive substrate to create a helicalelement. This has the advantage of shortening the apparent length, butit will also reduce the antenna's bandwidth. Like an inductor, thetighter the coil and the higher the Q, the smaller the bandwidth.

It is stressed, however, that the present system is not limited todipole elements, but rather any suitable structure can be utilized.Crossed dipoles are used in many mobile base station antennas to provideorthogonal, dual linear polarization for polarization diversity. Thelens may be fed by any style of radiating antenna element such as thepatch antenna, open-ended waveguide antenna, horn antenna, etc.Generally, low gain antennas are selected as feed elements for thespherical lens in order to maximize the lens efficiency and thedirectivity of the secondary radiation beam. The present invention isalso capable of operating with multiple polarizations thanks to thespherically symmetric nature of the dielectric lens. The radiatingantenna elements may exhibit single linear, dual linear, or circularpolarization. Multiple polarizations may be important for future 5Gsystems where polarization selection may be different depending on theoperating frequency and the intended user. Therefore, the multi-beamantenna should perform sufficiently no matter the desired polarizationwith a minimum of 20 dB isolation between orthogonal polarizations.

In one embodiment, a half-wave dipole antenna receives a radio signal.The incoming radio wave (whose electric field is E) causes anoscillating electric current within the antenna elements, alternatelycharging the two sides of the antenna positively (+) and negatively (−).Since the antenna is one half a wavelength long at the radio wave'sfrequency, the voltage and current in the antenna form a standing wave.This oscillating current flows down the antenna's transmission linethrough the radio receiver (represented by resistor R).

The antenna can be crossed dipole elements. In one illustrativenon-limiting embodiment in FIG. 3A, the elements 111 are fabricated fromdouble sided printed circuit board (PCB) material where the +45° dipolePCB material 112 a is positioned substantially orthogonal (90°) withrespect to the −45° dipole PCB material 112 b. Thus as best shown inFIG. 3B, the first portion 112 extends substantially orthogonal to thesecond portion 114 to form a general T-shape. The first portion 112 iscoupled with and extends substantially orthogonal to the inner platformsurface 124. The second portion 114 is coupled with the first portion112 and extends substantially parallel to and spaced apart from both theinner platform surface 120 and the outer lens body surface 104. Thefocal surface 130 is aligned with the phase center of the elementfeeding the lens. As shown, the focal surface 130 can be aligned withthe middle of the second portion 114, though need not be aligned withthe middle of the second portion 114. The particular PCB material may bechosen from a plethora of available materials, but the material isgenerally chosen to have a dielectric constant value in the range ofεr=2-5 with a low dielectric loss tangent. For example, a suitablematerial would be Arlon 25N with a dielectric constant εr≈3.38 and aloss tangent tan δ≈0.0025. The dipole arms 114 a/114 b and the baluns116 a/116 b are generally copper and can be formed by etching or millingaway the copper cladding from the PCB material. The dipole arms 114a/114 b form the radiating structures for the antenna while the baluns116 a/116 b provide a transition from the feed network generating theproper phase on each dipole arm as those skilled in the art canappreciate. Any suitable structure and arrangement for the baluns 116can be utilized. Instead of the PCB material, the electricallyconductive material used in the antenna can be made of or include anelectrically conductive fabric, which can include any kind of electronictextile or “e-textile”. E-textiles can include any textile that can beapplied to the physical manipulation of electrical or electromagneticsignals or radiation; most often, this is associated with devices thatincorporate one or more electronic devices. Conductive fabric used inthe manufacture of c-textiles can have a surface resistance ranging froma low of about 0 Ω/sq. to a high of about 1 Ω/sq. and can provide atleast partial shielding and/or at least partial blocking ofelectromagnetic wave transmission or radiation. Many methods forconstruction of these conductive fabrics exist, such as weaving metal,metalized fiber strands, or other conducting fabric strands intonon-conductive fabric. Another method for constructing conductivefabrics includes spraying and/or painting conductive material onto abase layer, where the base layer is usually non-conductive. Metals thatcan be used in the construction of electronic textiles can include, butare not limited to, copper, nickel, gold, silver, steal, zinc, tin,tungsten, iron, iridium, aluminum, alloys thereof, or other conductiveelements. Metalized fiber strands can include polymers coated withmetal. Other conducting fabric strands can include electricallyconducting polymers or plastics. Electronic textiles can includemultiple metalized fibers wrapped together to form electricallyconductive strands. Electronic textiles can include nano-tubes or otherNano-particles that have advanced electronic function. In anotherembodiment, the electrically conductive region 130 can be made usingmetal meshes, such as a copper wire or gold wire mesh. Just as there canbe many different means to creating conductive fabrics for use withc-textiles, numerous non-conductive materials can be used in conjunctionwith the aforementioned conductive materials. Suitable non-conductivematerials can include, but is not limited to, nylon, NOMEX®, KEVLAR®,aromatic polyamide polymers, polyester, cotton, Rip-stop nylon, canvas,other common textiles or materials having bulk electrical propertiesfitting the description a good non-conductor, or combinations thereof.The non-conductive material can be in the form of a web having air or avacuum dispersed through non-conductive strands.

The antenna element 111 can be a spring-like material, which may bedescribed as any elastic body or device that recovers its original shapewhen released after being distorted. The spring-like material of theantenna can be deformable and can be conductive, non-conductive, orpartially conductive and partially non-conductive. For example, thespring-like material can include, but is not limited to, plastic, metal,rubber, fiber, fiberglass, carbon, carbon-glass composites, or acombination thereof. Other materials that can be used in the supportmember include shape memory allows, shape memory polymers, or acombination thereof. Suitable shape memory alloys can include, but arenot limited to, Ag—Cd 44/49, Au—Cd 46.5/50, Cu—Al—Ni, Cu—Sn, Cu—Zn,Cu—Zn—Si, Cu—Zn—Al, Cu—Zn—Sn, Fe—Pt, Mn—Cu 5/35, Fe—Mn—Si, Pt alloys,Co—Ni—Al, Co—Ni—Ga, Ni—Fe—Ga, Ti—Pd, Ni—Ti, Ni—Mn—Ga, Fe—Ni, Fe—Pt,Fe—C, Fe—Ni—C, Fe—Cr—C, Au—Mn, In—TI, In—Cd, In—Pb, Fe—Pd, Ni—Al, Ti—Mo,Ti—V, Cu—Al, Ti—Ta, or combinations thereof.

In one embodiment shown in FIG. 4A, a plurality of antenna element 111can be placed on a substrate in a bee-eye arrangement. In one embodimentof FIG. 4A, the array is fixed. In another embodiment, each element 214is motorized and steerable. In such motorized embodiment, an array ofmotors (such as linear motors) can move up and down and affect thedirectionality of the associated antenna element 111. In the embodimentof FIGS. 4B-4C, liquid lenses are provided on both sides, and antennaelement 111 directionality can be fine tuned to optimize communication.The liquid lenses enable the antenna to move similar to the eyes to gazetoward the corresponding remote 5G cellular transceiver.

Turning now to FIG. 4A, a lens antenna system 210 is shown coupled to aconventional deployed small cell. As used herein, the term “lens” canrefer to any three-dimensional structure, through which electromagneticwaves can pass and that uses either refraction or diffraction to controlthe exiting aperture distribution as a function of its position andshape. As used herein, the terms “Fresnel lens” or “Fresnel zone plate”can refer to a type of lens that produces focusing and imaging ofelectromagnetic waves using diffraction, rather than refraction. It isnoted that a lens and hence, a Fresnel lens, are not antennas. Anantenna is a transducer that transmits or receives electromagneticwaves. Conversely, a Fresnel lens does not transmit or receiveelectromagnetic waves. As stated above and as will be discussed in moredetail supra, electromagnetic waves are passed through a Fresnel lenswherein said electromagnetic waves may be focused into Fresnel zoneregions. The lens system 210 includes tubular waveguide lens cells,indicated generally at 214, interconnected to form a collapsiblehoneycomb array. The array 214 is constructed of a plurality of cells,each of the cells in the cell array 214 is hexagonal in cross-section asshown at 24 in FIG. 2a with equiangular sides having uniform lengths ofabout three inches. Each hexagonal cell preferably has a length uniformwith other array cells of from six inches to twelve inches, dependingupon the particular application and the frequency of the signals to befocused.

Each antenna element can rest on a corresponding actuator to adjust theaiming direction. Alternatively, a group of elements can be moved by oneactuator to save cost. Thus, in one system 210, sub-groups of nearlyantenna elements can be aimed/focused as a group.

One embodiment mounts the antenna elements on a hard lens at the frontand a softer, flexible plastic sheet at the back. In between is a layerof viscous liquid. By pumping more of less of this liquid between thelayers, the system can custom fit the curvature of the lens, thus finelyaiming the antenna at a target for tuned reception. The system cancalibrate the connection by examining the RSSI and TSSI and scan themoveable lens until the optimal RSSI/TSSI levels are reached. In anotherembodiment, a group of antennas can be moved to optimize datatransmission. Two of the lenses can be placed on opposite sides forcommon control if desired.

FIG. 4B is a sectional view illustrating a liquid lens in which antennamount portions 212 and 213 provided on the opposite ends of theprotection member 201, and are formed of a member that is different fromthat of the protection member 201. The antenna element 111 is positionedon the liquid lens and can be pointed using the liquid lens, similar toan eye gaze. The antenna mount portions 212 and 213 are pressed bycovering members 227 and 228 each having a hole. The covering members227 and 228 are fixed to the protection member 201 by screws 229 and230, respectively. To prevent the liquids 204 and 205 from leakingthrough a gap between the protection member 201 and antenna mountportions 212 and 213, O-rings 231 and 232 are provided between theprotection member 201 and the transparent portions 212 and 113.

FIG. 4C is a sectional view illustrating a liquid lens in which antennamount portions 214 and 215 provided on the opposite ends of theprotection member 201. During operation, an elastic member in the middleof the chamber can be used for change in surface shape of the elasticmember 206 when the elastic member 206 is moved. In the case of using anincompressible liquid, since volumes of the liquids 204 and 205 areconstant before and after the movement of the elastic member 106, thesurface shape of the elastic member changes in such a manner that thevolumes of the liquids 204 and 205 are constant. Since the surface shapeof the elastic member 206 is uniquely determined by the distance ofmovement of the elastic member 206, the elastic member 206 can becontrolled to form a desired surface shape by controlling the positionof the elastic member 206.

In an alternate embodiment, instead of liquid lens, an array ofactuators can be used to aim the antenna array. Based on the desiredantenna directionally, one embodiment of the antenna actuator 1004 formsa 3D shape by having an array of computer controlled moveable pins whoseheight is adjusted in accordance with the CAD design file, and theoverall shape is smoothed by a Lycra sheet or felt sheet. The pins orrods lift the felt or Lycra sheet to form a 3D shape that points theantenna at a desired aim. In this embodiment, an array of N×N microhydraulic actuators can be used to form the shape. This embodiment is adense hydraulic planar pin-rod matrix array. Another embodiment actuatesan N×N pin-rod matrix driven by servomotors. In either case, eachpin-rod is controlled individually, similar to pixels on a screen exceptthat the pixel has height as well.

In some embodiments, an optional automatic focus training mode isprovided, wherein the 5G antennas automatically learn the compensationnecessary for an individual's unique desired data speed, and thusgenerate at least some of the desired data speed information for theuser. In some embodiments, the process for the focus training mode mayoperate in the matter of an autorefractor (as referred to an automatedrefractor), an autorefractor being a computer-controlled machine thatmay be used during 5G set up, the autorefractor operating to provide anobjective measurement of a 5G refractive error and desired data speedfor 5G antenna. In some embodiments, 5G antennas may generate awavefront distortion map for use in evaluating the performance of the 5Gantennas.

In some embodiments, an algorithm for a focus training process for theautofocus 5G antennas may include, for each of multiple RF views, suchas RF views of targets that are near, medium, and far distances:

(a) Adjusting the lens to determine a setting that provides maximum RFclarity, the setting being a lens offset value; and

(b) Saving the lens offset value for use in the automatic adjusting modefor the 5G antennas.

In some embodiments, the focus training mode may include menus that areprovided to a user during a focus training process, wherein the focustraining process may include the provision of alternative focus settingsfor a user and providing an inquiry to the user regarding which settingis better in order to hone in on an optimal desired data speed using abinary tree or a learning machine/neural network, among others.

In one embodiment of a process for a focus training mode of autofocus 5Gantennas, upon the 5G antennas entering a training mode, a menu sequencefor training is loaded to guide a user through the RF focus trainingprocess. In some embodiments, the system may instruct the user to view a5G object at a first distance. In some embodiment, the view may be ofsymbols or images generated by the 5G antennas that assist a viewer indetermining whether an image is in focus. In some embodiments, a set ofviews with alternative lens settings are provided to the viewer 546,where the set of view may include a first view at a first focus settingand a second view at a second focus setting. In some embodiments, arequest is provided to the viewer to choose one view of the set of viewsas being better focused 548.

In some embodiments, if a certain speed threshold for the testing is notyet met, the process continues with provision of alternative RF focussettings to continue working towards an optimal focus setting for thedistance. In some embodiments, if the speed threshold is met, then, ifthere is an additional distance to test, then the system communicateswith an object at a next distance, with the processing returning toproviding images with alternative lens settings for view by the user. Insome embodiments, if there are no more additional distances to test,then the desired data speed of the user is determined based on the userchoices of focus settings, the determined vision desired data speed isstored in the autofocus 5G antennas or in a companion device, and thetraining mode is ended.

Coinciding with a signing-off of global standardizations for 5G radiotechnology by 3GPP is the need for faster 6G wireless. One hundredgigabits-per-second speeds will be streamed to 6G users with very lowlatency. For comparison, the telecommunications union ITU's IMT-2020 hasprojected that 5G speeds is around 20 Gbps. In contrast, 4G atfrequencies below a few gigahertz, provides generally available averagedownloads speeds at rates below 20 Mbps. High frequencies, in the rangeof 100 GHz to 1 THz (terahertz) are used for 100 Gbps 6G.

To allow for ease of upgrading from 5G to 6G, reconfigurable antennascan used. A reconfigurable antenna is an antenna capable of modifyingits frequency and radiation properties dynamically, in a controlled andreversible manner. The liquid metal antenna discussed above is anexample of a reconfigurable antenna. Reconfigurable antennas can tune todifferent frequency bands. Such an antenna would not cover all bandssimultaneously, but provides narrower instantaneous bandwidths that aredynamically selectable at higher efficiency than conventional antennas.Such tunable-antenna technology is an enabler for software-definableradios, the RF front ends of which must be reprogrammable on the fly.

FIG. 5A-5B shows two exemplary 5G IC (such as power amplifier) withintegrated cooling heat spreader and antenna structures. FIG. 5Aillustrates an example of such a boiling cooler with a vessel 220enclosed by a body-shell 271 comprising multiple 5G antennas such asupper chambers 221 and 222 in irregular shapes and different heights,for example, on top of a common base chamber. The boiling cooler alsocomprises a boiling enhancement surface 241 on a thermally conductiveside shell 231 (a part of the body-shell 271), partially filled liquidcoolant 251. Vapor generated from boiling helps spread heat over allextended space 261 adding extra pathway for cooling through convection.In some cases, the electric circuit module boards and/or system linecards, on which many electric or photonic components/devices are tightlypacked, have very stringent requirements in the mechanical design forthe associated or integrated coolers. While the embodiment shown in FIG.5A is shown with the die 241 for horizontal operation, it can be placedfor vertical operation.

FIG. 5B illustrates current two-phase cooling chamber with integratedelectro-deposited surfaces that utilize boiling (evaporation),condensation, and capillary pumping action. Liquid will be vaporized(boiled) from the electro-deposited surface designed for boiling(evaporation) enhancement, then the vapor will be condensed at theenhanced surface for condensation by electro-deposition on top plate.Finally the condensed liquid will be supplied back to the heated regionby another electro-deposited surface aimed for capillary pumping action.The system of FIG. 5B ensures that the heat absorbing surface or coatingcontacts the liquid coolant to ensure an efficient transfer of heat fromthe heat source to the liquid and to the rest of the module. The systemallows the integrated circuit to run at top performance while minimizingthe risk of failure due to overheating. The system provides a boilingcooler with a vessel in a simplified design using inexpensive non-metalmaterial or low cost liquid coolant in combination with a boilingenhancement surface or coating.

FIG. 6 shows an exemplary simplified massive MIMO system with antennaports for user streams. Each user stream is a spatial stream of data.Each spatial stream that may include data from multiple users that areallocated different frequencies within the same spatial stream, in someembodiments. Further, a given user may be allocated multiple spatialstreams, in some embodiments. Therefore, the number of userscommunicating with the system may or may not correspond to the number ofantenna ports. In some embodiments, MIMO RX is configured to perform thefunctionality of channel estimator, MIMO detector, link qualityevaluator, etc. In some embodiments, MIMO TX is configured to performMIMO precoder.

During operation, a base station selects a number of antennas from amonga plurality of available antennas for use in MIMO wirelesscommunications. For example, the system may include 128 antennas but thebase station may select to use only 64 antennas during a given timeinterval based on current operating conditions. The decision of how manyantennas to use may be based on user input, a number of users currentlyin a cell, wireless signal conditions, bandwidth of currentcommunications, desired testing conditions, etc. The base station mayselect different numbers of antennas at different times, e.g., a largernumber during peak communications intervals and a smaller number duringtrough intervals. The base station determines a number of processingelements for processing received signals from the selected number ofantennas. In the illustrated embodiment, this is based on the number ofantennas selected and one or more threshold throughput values. In someembodiments, this determination may be based on any of variousappropriate parameters in addition to and/or in place of the parameters,including without limitation: the processing capacity of each processingelement, the amount of data per sample or entry for various information,a sampling rate, the number of spatial streams, number of users, etc.Determining the number of processing elements may include determining anumber of parallel receive chains for MIMO RX. In some embodiments, eachreceive chain includes a configurable MIMO core and a configurablelinear decoder. The base station processes incoming wirelesscommunications using the determined number of processing elements. Thismay include applying a MIMO signal estimation techniques such as MMSE,ZF, or MRC and decoding received data streams. After processing, thedecoded data from the determined number of processing elements may bereformatted and routed and transmitted to appropriate destinations(e.g., via another network such as a carrier network, the Internet,etc.). In some embodiments, the base station dynamically switchesbetween different MIMO signal estimation techniques, e.g., based on userinput, operating conditions, or any of various appropriate parameters.

The neural network control of the MIMO system may, in some embodiments,facilitate testing of MIMO base stations, reduce power consumptionduring MIMO communications, allow for flexibility in capacity, allow forflexibility in MIMO signal estimation, allow routing around defectiveprocessing elements or antennas, etc. In some embodiments, the basestation may also be dynamically or statically customized for a widevariety of operating conditions and/or research needs and may beconfigured for real-time processing.

The massive MIMO system may be included in base station, for example,and the TXRX data is provided to the neural network plane foroptimization. Data on the operation of any of the subunits of the MIMOsystem can be captured for learning system behavior and for optimizingthe system by the neural network or learning machine. In one embodiment,the subsystem includes front-end TX/RX units, antenna combiner, antennasplitter, bandwidth splitter, bandwidth combiner, channel estimator,MIMO detector, and MIMO precoder. Other subsystems of include additionalMIMO detectors, MIMO precoders, bandwidth splitters, and bandwidthcombiners. MIMO processing can be distributed among various processingelements. This may allow baseband processing to be partitioned acrossmultiple FPGAs, for example. This may facilitate scaling of massive MIMOsystems far beyond what a single centralized processing unit couldachieve for real-time baseband processing. For uplink symbols, eachTX/RX may be configured to digitize the received RF signals, performanalog front-end calibration and time/frequency synchronization, removethe cyclic prefix (CP), and perform FFT OFDM demodulation and guard-bandremoval. This may result in frequency domain pilot and unequalized datasymbol vectors, which is provided to antenna combiner. For downlinksymbols, each TX/RX may be configured to perform ODFM processing. Theantenna combiner, bandwidth splitter, MIMO precoder, bandwidth combiner,and antenna splitter are each located on a different SDR element thatalso implements one of TX/RXs. In one embodiment, channel estimator andMIMO detector are located on another SDR element that also implementsone of TX/RXs. In various embodiments, the various elements of FIG. 3may be partitioned among various hardware elements configured to performthe disclosed functionality. The hardware elements may be programmableand/or include dedicated circuitry. Antenna combiner is configured toreceive the yet unequalized OFDM symbols from each TX/RX and combinesthem into a signal sent to bandwidth splitter. This combines the signalsfrom up to N antennas in the subsystem. Combining this informationbefore further processing may allow the system to stay within throughputconstraints and may reduce the number of peer-to-peer connectionsbetween SDRs, for example. In some embodiments, the number of antennasfor which signals are combined by each antenna combiner is dynamicallyconfigurable. Bandwidth splitter is configured to split the receivedsignals into separate bandwidth portions and send the portions to MIMOdetectors in different subsystems. Thus, in the illustrated embodiment,processing is distributed across different processing elements that eachprocess data for a different frequency band. Each bandwidth portion mayinclude one or more subcarriers and the portions may or may not benon-overlapping. In some embodiments, the number of bandwidth portionsand the size of each portion is configurable, e.g., based on the numberof antennas, current number of users in communication, etc. In otherembodiments, processing may be distributed among processing elementsacross different time slices in addition to and/or in place of splittingby frequency. In some embodiments, bandwidth splitter is replaced with atime-slice splitter. Post-FTT subcarrier processing in OFDM may beinherently independent, allowing subsequent processing to be performedin parallel by different processing elements. The output of TX/RX can beprovided directly to bandwidth splitter and an output of bandwidthcombiner is provided directly to TX/RX. In other embodiments, theseoutputs may be provided to antenna combiner and antenna splittersimilarly to the other signals. In embodiments in which TX/RX andbandwidth splitter share the same SDR element and TX/RX and bandwidthcombiner share the same SDR element, however, the illustrated couplingmay conserve I/O resources. MIMO detector is configured to use anestimated channel matrix (e.g., based on uplink pilot symbols) to cancelinterference and detect frequency-domain symbols from each mobiledevice. As shown, in some embodiments MIMO detector is configured toprocess signals in a given bandwidth from multiple subsystems of system300. In the illustrated embodiment, MIMO detector is configured to sendthe detected signals to channel estimator and to link quality evaluator(included in a central controller in some embodiments) for furtherprocessing.

Channel estimator is configured to perform channel estimation for itsfrequency portion for a number of mobile devices, e.g., to producesoft-bits (also referred to as log-likelihood ratios (LLRs)) and providethem to link quality evaluator (coupling not shown). In someembodiments, multiple decoders are implemented, including a turbodecoder, for example. MIMO precoder is configured to receive downlinkdata from data source and precode the data based on channel estimates(e.g., estimated reciprocity calibration weights) from channelestimator. In some embodiments, the MIMO precoders are configured toperform precoding on different frequency portions of the downlink data.In some embodiments (not shown), the MIMO precoders in system 300 areconfigured to perform precoding on different time portions of thedownlink data. Bandwidth combiner is configured to combine signals atdifferent bandwidths from multiple MIMO precoders and send the data toantenna splitter. This may result in a complete set of precoded data fortransmission from the separately processed bandwidth portions. In otherembodiments, bandwidth combiner is configured to combine datacorresponding to separately-processed time slices in place of or inaddition to combining separately-processed frequency portions. Antennasplitter is configured to split the received signal and provide thesplit signal to TX/RXs for OFDM processing and transmission to mobiledevices or UEs. The set of antennas to which antenna splitter isconfigured to provide signals is dynamically configurable, in someembodiments (e.g., the number of antennas and/or the particular antennasin the set). Thus, in some embodiments, the set of processing elementsconfigured to perform distributed processing for particular antennasand/or users is dynamically configurable. Link quality evaluator isincluded in a central control unit and is configured to measure linkquality using one or more of various metrics such as bit error rate(BER), error vector magnitude (EVM), and/or packet-error rate (PER).

In various embodiments, the MIMO system is highly configurable, e.g.,based on user input and/or by the neural network based on traininghistory and current operating conditions. In some embodiments, variousdisclosed configuration operations are performed automatically. In someembodiments, the number of processing elements used at a given time toperform distributed processing for a set of users or a set of antennasis configurable. In some embodiments, the number of antennas used tocommunicate with each UE is configurable and/or dynamically determined.In some embodiments, the processing elements configured to performdifferent functionality described above is configurable. For example,the antenna combiner function may be moved from one FPGA to another FPGAor performed by multiple FPGAs. In some embodiments, the routing of databetween processing elements is configurable, e.g., to avoidmalfunctioning antennas and/or processing elements. In some embodiments,for example, system includes 16, 32, 64, 100, 128, 256, or moreantennas. In some embodiments, components of system are modular suchthat the number of antennas may be increased by adding additionalcomponents, and each antenna parameters can be captured and learned bythe neural network for subsequent optimization during live operation.

FIG. 7A shows an exemplary 5G control system that uses learning machinesor neural networks to improve performance. The neural network planeprovides automated intelligence to select the best operations givenparticular mobile device or wireless client needs. By enabling bothclient and infrastructure intelligence, the 5G networked system couldreason about the deficiencies it suffers from, and improve itsreliability, performance and security. By pushing more network knowledgeand functions to the end host, the 5G clients could play more activeroles in improving the user-experienced reliability, performance andsecurity. The neural plane sits above the data plane, control plane andmanagement plane. The Control Plane makes decisions about how to set upthe antenna settings and where traffic is sent. Control plane packetsare destined to or locally originated by the router itself. The controlplane functions include the system configuration, management, andexchange of routing table information. The route controller exchangesthe topology information with other routers and constructs a routingtable based on a routing protocol, for example, RIP, OSPF or BGP.Control plane packets are processed by the router to update the routingtable information. It is the signaling of the network. Since the controlfunctions are not performed on each arriving individual packet, they donot have a strict speed constraint and are less time-critical. The DataPlane or Forwarding Plane Forwards traffic to the next hop along thepath to the selected destination network according to control planelogic. Data plane packets go through the router. The routers/switchesuse what the control plane built to dispose of incoming and outgoingframes and packets. The management plane configures, monitors, andprovides management, monitoring and configuration services to, alllayers of the network stack and other parts of the system. It should bedistinguished from the control plane, which is primarily concerned withrouting table and forwarding information base computation.

On the client side, the system collect runtime, fine-grained information(protocol states, parameters, operation logic, etc.) from full-stackcellular protocols (physical/link layer, radio resource control,mobility management, data session management) inside the 5G device orphone, and such information is provided to the neural network plane. Oneembodiment extracts cellular operations from signaling messages betweenthe device and the network. These control-plane messages regulateessential utility functions of radio access, mobility management,security, data/voice service quality, to name a few. Given thesemessages, it further enables in-device analytics for cellular protocols.The system infers runtime protocol state machines and dynamics on thedevice side, but also infer protocol operation logic (e.g., handoffpolicy from the carrier) from the network. The system collects rawcellular logs from the cellular interface to the device user-space atruntime, and then parses them into protocol messages and extracts theircarried information elements. The parsed messages are then fed to theanalyzer which aims to unveil protocol dynamics and operation logics.Based on the observed messages and the anticipated behavior model (fromcellular domain knowledge), the analyzer infers protocol states,triggering conditions for state transitions, and protocol's takenactions. Moreover, it infers certain protocol operation logic (e.g.,handoff) that uses operator-defined policies and configurations. Itoffers built-in abstraction per protocol and allows for customize theseanalyzers. On the management plane, the system captures full-stacknetwork information on all-layer operations (from physical to datasession layer) over time and in space. This is achieved by crowdsourcingmassive network data from mobile devices temporally and spatially. Aninstability analyzer reports base station stability and reachability toavoid getting stuck in a suboptimal network. The instability analyzermodels the decision logic and feeds this model with real configurationscollected directly from the device and indirectly from the serving cell,as well as dynamic environment settings created for various scenarios.For example, antenna parameters (pointing direction, frequency, andRSSI/TSSI and channel) are captured to identify optimal settings for aparticular device/client. The system can model cellular protocols isderived from the 5G standards for each protocol. This works particularlywell for non-moving client devices such as 5G modems/routers and mobilephones that operate within a house or office most of the time, forexample. When the mobile device is on the move, population data can beused to optimize antenna and communication parameters to derive theoptimal connection for the device or client. For example, the neuralnetwork layer can identify clients using the Ultra Reliable Low LatencyCommunications specification (such as full car automation, factoryautomation, and remote-controlled surgery where reliability andresponsiveness are mandatory) and control the 5G network to respond toURLLC requests by delivering data so quickly and reliably thatresponsiveness will be imperceptibly fast by selecting appropriateantenna parameters and settings for URLLC from the tower to the clientdevice.

In addition to the neural network plane, the logical functionarchitecture includes a data plane, a control plane, and a managementplane. The control plane includes a software defined topology (SDT)logical entity configured to establish a virtual data-plane logicaltopology for a service, a software defined resource allocation (SDRA)logical entity configured to map the virtual data-plane topology to aphysical data-plane for transporting service-related traffic over thewireless network, and a software defined per-service customized dataplane process (SDP) logical entity configured to select transportprotocol(s) for transporting the service-related traffic over a physicaldata-plane of the wireless network. The management plane may includeentities for performing various management related tasks. For example,the management plane may include an infrastructure management entityadapted to manage spectrum sharing between different radio accessnetworks (RANs) and/or different wireless networks, e.g., wirelessnetworks maintained by different operators. The management plane mayalso include one or more of a data and analytics entity, a customerservice management entity, a connectivity management entity, and acontent service management entity, which are described in greater detailbelow.

The neural network plane works with network functions virtualization(NFV) to design, deploy, and manage networking services. It is acomplementary approach to software-defined networking (SDN) for networkmanagement. While SDN separates the control and forwarding planes tooffer a centralized view of the network, NFV primarily focuses onoptimizing the network services themselves. The neural network planeautomates the optimization level to the next automation and efficiency.

A virtual service specific serving gateway (v-s-SGW) can be done. Thev-s-SGW is assigned specifically to a service being provided by a groupof wirelessly enabled devices, and is responsible for aggregatingservice-related traffic communicated by the group of wirelessly enableddevices. In an embodiment, the v-s-SGW provides access protection forthe service-related traffic by encrypting/decrypting data communicatedover bearer channels extending between the v-s-SGW and thewirelessly-enabled devices. The v-s-SGW may also provide a layer two(L2) anchor point between the group of wirelessly-enabled devices. Forexample, the v-s-SGW may provide convergence between the differentwireless communication protocols used by the wirelessly-enabled devices,as well as between different wireless networks and/or RANs being accessby the wirelessly-enabled devices. Additionally, the v-s-SGW may performat least some application layer processing for the service relatedtraffic communicated by the wirelessly-enabled devices. Aspects of thisdisclosure further provide an embodiment device naming structure. Forthe v-s-SGW. Specifically, a v-s-SGW initiated on a network device isassigned a local v-u-SGW ID. Outgoing packets from the v-u-SGW IDinclude the local v-u-SGW ID and a host ID of the network device.Accordingly, recipients of those outgoing packets can learn the localv-u-SGW ID and the host ID associated with a particular v-s-SGW, andthereafter send packets to the v-s-SGW by including the local v-u-SGW IDand the host ID in the packet header.

Location tracking as a service (LTaaS) can be provided. The LTaaSfeature may track locations of user equipment's (UEs) via a devicelocation tracking as a service (LTaaS) layer such that locations of theUEs are dynamically updated in a LTaaS layer as the UEs move todifferent locations in the wireless networks. In some embodiments, theLTaaS layer consists of a centralized control center. In otherembodiments, the LTaaS layer consists of a set of distributed controlcenters positioned in the wireless network, e.g., an applicationinstalled on a network device, such as a gateway or AP. In yet otherembodiments, the LTaaS layer comprises both a central controller centerand regional control centers. In such embodiments, the central controlcenter may be updated periodically by the regional control centers,which may monitor UE movement in their respective wireless networks. Inembodiments, the LTaaS layer may monitor general locations of the UEs.For example, the LTaaS layer may associate the UE's location with anetwork device in a specific wireless network, e.g., an access point, aserving gateway (SGW), etc.

Content may be cached in network devices of wireless network or radioaccess network (RAN) in anticipation that a mobile device or user willwant to access the content in the future. In some embodiments, a contentforwarding service manager (CFM) may select content to be pushed to acaching location in the wireless network based on the popularity ofavailable content stored in one or more application servers. The networkdevice may comprise a virtual information-centric networking (ICN)server of an ICN virtual network (VN), and may be adapted to provide thecached content to a virtual user-specific serving gateway (v-u-SGW) of aserved user equipment (UE) upon request. Notably, the cached content isstored by the network device in an information-centric networking (ICN)format, and the v-u-SGW may translate the cached content from the ICNformat to a user-specific format upon receiving the cached contentpursuant to a content request. The v-u-SGW may then relay the cachedcontent having the user-specific format to a served UE. After thecontent is pushed to the network device, the content forwarding servicemanager (CFM) may update a content cache table to indicate that thecontent has been cached at the network device. The content cache tablemay associate a name of the content with a network address of thenetwork device or the virtual IVN server included in the network device.The ICN VN may be transparent to the served UE, and may be operated byone of the wireless network operators or a third party. These and otheraspects are described in greater detail below.

The management plane 310 may include entities for performing variousmanagement related tasks. In this example, the management plane 330includes a data and analytics entity 311, an infrastructure managemententity 312, customer service management entity 313, a connectivitymanagement entity 314, and a content service management entity 315. Thedata and analytics entity 311 is configured to provide data analytics asa service (DAaaS). This may include manage on-demand network statusanalytics and on-demand service QoE status analytics for a particularservice, and providing a data analytics summary to a client. Theinfrastructure management entity 312 may manage spectrum sharing betweendifferent radio access network (RANs) in a wireless network, or betweenwireless networks maintained by different operators. This may includewireless network integration, management of RAN backhaul and access linkresources, coordination of spectrum sharing among co-located wirelessnetworks, access management, air interface management, and device accessnaming and network node naming responsibilities.

The customer service management entity 313 may provide customer servicefunctions, including managing customer context information,service-specific quality of experience (QoE) monitoring, and chargingresponsibilities. The connectivity management entity 314 may providelocation tracking as a service (LTaaS) over the data plane of thewireless network. The connectivity management entity 314 may also haveother responsibilities, such as establishing customized and scenarioaware location tracking scheme, establishing software defined andvirtual per-mobile user geographic location tracking schemes, andtriggering user specific data plane topology updates. The contentservice management entity 315 may manage content caching in the wirelessnetwork. This may include selecting content to be cached in RAN,selecting caching locations, configuring cache capable network nodes,and managing content forwarding. In some embodiments, the managementplane may also include a security management entity that is responsiblefor network access security (e.g., service-specific security, customerdevice network access protection, etc.), as well as inter-domain andintra-domain wireless network security.

The control plane 320 may include entities for performing variouscontrol related tasks. In this example, the control plane includes asoftware defined topology (SDT) logical entity 322, a software definedresource allocation (SDRA) logical entity 324, and a software definedper-service customized data plane process (SDP) logical entity 326. TheSDT entity 322, the SDRA logical entity 324, and the SDP logical entity326 may collectively configure a service-specific data plane forcarrying service-related traffic. More specifically, the softwaredefined topology (SDT) logical entity 322 is configured to establish avirtual data-plane logical topology for a service. This may includeselecting network devices to provide the service from a collection ofnetwork devices forming the data plane 330. The software definedresource allocation (SDRA) logical entity 324 is configured to map thevirtual data-plane topology to a physical data-plane for transportingservice-related traffic over the wireless network. This may includemapping logical links of the virtual data-plane topology to physicalpaths of the data plane. The software defined per-service customizeddata plane process (SDP) logical entity 326 is configured to selecttransport protocol(s) for transporting the service-related traffic overa physical data-plane of the wireless network. The transport protocolsmay be selected based on various criteria. In one example, the SDPlogical entity selects the transport protocol based on a characteristicof the service-related traffic, e.g., business characteristic, payloadvolume, quality of service (QoS) requirement, etc. In another example,the SDP logical entity selects the transport protocol based on acondition on the network, e.g., loading on the data paths, etc.

The SDT entity 322, the SDRA logical entity 324, and the SDP logicalentity 326 communicate with the neural network plane to optimize thesystem configuration (including antenna pointing/setting/redundancyassignment, among others), and they may also have other responsibilitiesbeyond their respective roles in establishing a service-specific dataplane. For example, the SDT entity 322 may dynamically define keyfunctionality for v-s-SGWs/v-u-SGWs, as well as enable mobile VNmigration and provide mobility management services. As another example,the SDRA logical entity 324 may embed virtual network sessions, as wellas provide radio transmission coordination. One or both of the SDTentity 322 and the SDRA logical entity 324 may provide policy andcharging rule function (PCRF) services.

The SDT entity 322, the SDRA logical entity 324, and the SDP logicalentity 326 may collectively configure a service-specific data plane forcarrying service-related traffic. Specifically, the SDT entity 322establishes a virtual data-plane logical topology for the service, theSDRA logical entity 324 maps the virtual data-plane topology to aphysical data-plane path for transporting service-related traffic overthe wireless network, and the SDP logical entity 326 select transportprotocol(s) for transporting the service-related traffic over thephysical data-plane.

In one example, the neural network can automatically allocate functionsin a mobile network based at least in part on utilization levels. Forexample, various components of the 5G network can include, but are notlimited to, a network exposure function (NEF), a network resourcefunction (NRF), an authentication server function (AUSF), an access andmobility management function (AMF), a policy control function (PCF), asession management function (SMF), a unified data management (UDM)function, a user plane function (UPF), and/or an application function(AF). For example, some or all of the functions discussed herein canprovide utilization levels, capability information, localityinformation, etc., associated with the various functions to a networkresource function (NRF) (or other component), for example, such that theNRF or other component can select a particular function of a pluralityof possible components providing the same function based on theutilization levels of the particular component. Thus, the system,devices, and techniques broadly apply to selecting network functions,and is not limited to a particular context or function, as discussedherein.

The neural network plane improves the functioning of a network by takinga global management view to optimize the network by reducing networkcongestion, dropped packets, or dropped calls due to overutilization ofresources. Further, the systems, devices, and techniques can reduce asize of components (e.g., processing capacity) by obviating or reducingany need to over-allocate resources to ensure spare capacity to reducecongestion. Further, selecting functions based on utilization levels canreduce signaling overhead associated with dynamically allocating a sizeof a virtual instance. In some instances, the architecture describedherein facilitates scalability to allow for additional components to beadded or removed while maintaining network performance. In someinstances, optimal functions can be selected in connection withhandovers (e.g., intracell or intercell) to balance a load on networkfunctions to provide improved Quality of Service (QoS) for networkcommunications. These and other improvements to the functioning of acomputer and network are discussed herein.

In one example, the neural network plane interacts with a user equipment(UE), an access and mobility management function (AMF), a networkresource function (NRF), a session management function (SMF), and a userplane function (UPF). The UE can transmit a registration request to theAMF. At a same or different time as the registration request, the UPFcan transmit utilization information to the NRF, which in turncommunicates with the neural network plane. In some instances, theutilization information can include information including, but notlimited to: CPU utilization level; memory utilization level; active orreserved bandwidth; a number of active sessions; a number of allowablesessions; historical usage; instantaneous usage; dropped packets; packetqueue size; delay; Quality of Service (QoS) level, antenna efficiency,antenna setting; and the like. Further, the utilization information caninclude a status of the UPF (e.g., online, offline, schedule formaintenance, etc.). In some instances, the UPF can transmit theutilization info at any regular or irregular interval. In someinstances, the UPF can transmit the utilization info in response to arequest from the NRF, and/or in response to a change in one or moreutilization levels above or below a threshold value.

Next, the UE can transmit a session request to the AMF, which in turncan transmit the session request to the SMF. In some instances, thesession request can include a request to initiate a voice communication,a video communication, a data communication, and the like, by andbetween the UE and other services or devices in the network. The SMF inturn talks to the neural network plane for management. Based on itslearned optimization, the neural network plane communicates instructionsto the SMF. At least partially in response to receiving command from theneural network plane, the SMF can transmit a UPF query to the NRF. Insome instances, the UPF query can include information including, but notlimited to: a type of session requested by the UE (e.g., voice, video,bandwidth, emergency, etc.); services requested by the UE; a location ofthe UE; a location of a destination of the session requested by the UE;a request for a single UPF or a plurality of UPFs; and the like.

In some instances, at least partially in response to receiving the UPFquery, the NRF can provide a UPF response to the SMF. In some instances,the UPF response can include one or more identifiers associated with oneor more UPFs that are available to provide services to the UE. In someinstances, the UPF response can be based at least in part on the sessionrequest and/or on the utilization info received from the UPF (as well asother UPFs, as discussed herein).

Based at least in part on the UPF response, the SMF can select a UPF(e.g., in a case where a plurality of UPF identifiers are provided tothe SMF) or can utilize the UPF provided by the NRF for a communicationsession. The SMF can select a UPF and can transmit a UPF selection tothe UPF that has been selected and/or designated to providecommunications to the UE.

At least partially in response to the UPF selection, the UPF can provideservices to the UE. As discussed herein, the UPF can facilitate datatransfer to and/or from the UE to facilitate communications such asvoice communications, video communications, data communications, etc.

In this manner, the neural network plane incorporates intelligence inproviding services to requests in a way that optimizes system hardwareand software resources and overall cost. Such services may include theedge processing services discussed above.

Next, an example process is disclosed for selecting a network function,such as a user plane function, based on utilization information learnedby the neural network. The example process can be performed by theneural network in conjunction with the network resource function (NRF)(or another component), in connection with other components discussedherein. First, the neural network receives utilization informationassociated with one or more network functions, such as one or more userplanes. Although discussed in the context of a UPF, this process applyequally to other network functions, such as a network exposure function(NEF), a policy control function (PCF), a unified data management (UDM),an authentication server function (AUSF), an access and mobilitymanagement function (AMF), a session management function (SMF), anapplication function (AF), and the like. In one example, user planes ina network can transmit utilization information to the NRF. In someinstances, the NRF can request utilization information from various UPFs(or any network function) on a regular schedule, upon receipt of arequest to initiate a communication, and then forwarding suchinformation to the neural network plane for training, for example. Insome instances, the UPF (or any network function) can transmitutilization information upon determining that a utilization level haschanged more than a threshold amount compared to a previous utilizationlevel. In some instances, utilization information can include, but isnot limited to, one or more of: CPU utilization (e.g., % utilization),bandwidth utilization, memory utilization, number of allowable sessions,number of active sessions, historical utilization information, expectedutilization levels, latency, current QoS of active sessions, and thelike. Further, in some instances, the neural network can receivecapability information associated with the user plane(s) (or any networkfunction), location information associated with the user plane(s) (orany network function), etc. Such utilization information, capabilityinformation, location information, etc. can be stored in a databaseaccessible by the NRF.

Next, the process can include receiving a request for a networkfunction, such as a user plane, the request associated with a userequipment. For example, a request can be received from a sessionmanagement function (SMF) or an access and mobility management function(AMF) (or any network function) for a user plane (or any networkfunction) to initiate a communication for a user equipment. In someinstances, the request can indicate a number of user planes (or anynetwork function) to be provided by the NRF (e.g., one or many). In someinstances, the request can include information associated with thecommunication, such as a type of the communication, locations of the UEand/or the destination of the communication, specialized services (e.g.,video encoding, encryption, etc.) requested in association with thecommunication, a bandwidth of the communication, a minimum QoS of thecommunication, and the like. In some instances, the request can be basedat least in part on a request initiated by the UE and provided to theAMF, the SMF, or any network function.

Operations by the neural network plane includes determining one or morenetwork functions (e.g., user planes) based at least in part on therequest and the utilization level. For example, the neural network planecan include determining that a first user plane (or any networkfunction) is associated with a first utilization level (e.g., 80% CPUutilization) and a second user plane (or any network function) isassociated with a second utilization level (e.g., 30% utilizationlevel). Further the neural network can include determining that thefirst utilization level is above a utilization threshold (e.g., 70% orany value) such that addition assignments of UEs to the UPF (or anynetwork function) may degrade a quality of connections associated withfirst UPF (or any network function). Accordingly, the neural network candetermine that the first UPF (or any network function) is to be selectedto provide data traffic for the UE.

As can be understood herein, there may be a variety of learningalgorithms or ways to determine which user planes (or any networkfunction) are to be selected as available for a communication. In someinstances, the neural network can include determining that theutilization level of the second user plane (or any network function)(e.g., 30%, discussed above) is lower than the utilization level of thefirst user plane (or any network function) (e.g., 80%, discussed above),and accordingly, can determine that the second user plane (or anynetwork function) is to be selected for the communication.

The neural network determines a plurality of user planes (or any networkfunction) that are available for a communication (e.g., that have autilization level below a threshold value). In some instances, the userplanes (or any network function) can be selected based on a proximity tothe UE, capabilities requested by the UE, etc. In some instances, theoperation 506 can include ranking or prioritizing individual ones of theplurality of user planes (or any network function) as most appropriateto be selected for the communication. The neural network then providesan identification of the one or more user planes (or any networkfunction) to a session management function (SMF) (or any selectingnetwork function) to facilitate a communication with the user equipment.For example, the operation by the neural network can include providingan address or other identifier corresponding to one or more UPFs (or anyone or more network functions) to an SMF (or any selecting networkfunction) in the network. In the case where one user plane (or anynetwork function) is provided, the SMF (or any selecting networkfunction) may utilize the explicit user plane (or any network function)identified by the NRF. In the case where more than one user plane (orany network function) is provided, the identification may includeadditional information to allow the SMF (or any selecting networkfunction) to select a user plane (or any network function), as discussedherein.

In another example for selecting a user plane function based onutilization information during a handover performed by the neuralnetwork (or another component), in connection with other componentsdiscussed herein. As usual, the neural network has utilizationinformation associated with one or more user planes which provideutilization information to NRF that in turn sends the info to the neuralnetwork layer. Upon receiving a request for a user plane, the neuralnetwork plane can include providing a first selection of at least onefirst user plane based at least in part on the request and theutilization information. The operation can include the providing,allocating, and/or selecting at least one user plane based onutilization information to balance a load across a plurality ofavailable user planes. In some instances, the operation 606 can includeestablishing a communication for the UE at a first radio access network(RAN) utilizing the first user plane. The neural network can receive anindication of a handover request. For example, as a UE moves about anenvironment, a signal quality can decrease between the UE and the firstRAN. Accordingly, the neural network can automatically change antennaparameters first based on learned parameters, and if that does notchange signal quality, the neural network can determine that a handovershould occur, based on one or more of, but not limited to: signalstrength of an anchor connection (e.g., a signal strength of the firstRAN); signal strength of a target RAN (e.g., a signal strength of asecond RAN); latency; UE speed/direction; traffic level(s); QoS; etc. Insome instances, the neural network determines that a new user plane isrequired/desired based at least in part on the indication of thehandover request. The neural network plane can provide a secondselection of at least one second user plane based at least in part onthe handover request and the utilization information. For example, theat least one second user plane can include user planes suitable andavailable to facilitate a communication with the UE. In some instances,the above operations can be repeated as a UE moves about an environment(and/or in response to initiate a handover based on UPF maintenance, forexample). That is, the operations can be repeated continuously orperiodically to determine a user plane to facilitate a communicationwhile balancing a load of the user planes.

The neural network plane can automatically configure the direction ofantennas and combine antennas in a massive MIMO antenna by firstfocusing the antenna on the UE device (which optimizes thedirectionality of the wireless link between the BS and the UE), and thentransmitting first pilot signals via each of multiple antennas of theUE; receiving antenna combining information from a base station (BS),the antenna combining information for combining the multiple antennasinto one or more antenna groups and an orthogonal sequence allocated toeach of the one or more antenna groups; and transmitting second pilotsignals to the BS using the allocated orthogonal sequences, wherein thesecond pilot signals are used for estimating downlink channels from theBS to the UE, wherein the antenna combining information is determinedbased on correlation of each of the multiple antennas obtained from thefirst pilot signals, and wherein a same orthogonal sequence is appliedto a second pilot signal transmitted via one or more of the multipleantennas belonging to a same antenna group. The neural network can senda preferred antenna combination that is sent to the BS based on one ormore of the following: 1) minimize a correlation between effectivechannels of the one or more antenna groups, 2) an amount of data to betransmitted, 3) second pilot signals. The second pilot signals can becaptured during different time periods than a time period during which aUE of belonging to a second UE group transmits the second pilot signals.The 1st pilot signal can be transmitted by the UE even after the UEconfigure the antenna combination. In this case, the base station mayconfigure new antenna combination based on the previous antennacombination (mapping between one logical channel and another logicalchannel). Based on this, the base station may determine antennacombining information and transmit it to the UE and to make each of thelogical (effective) channels become orthogonal to each other. The neuralnetwork plane monitors performance and can automatically reconfigure ormodify antenna combination when the SINR of the received signals becomepoor over a predetermined period of time. Based on this request, thebase station may receive the antenna combining information again andtransmit it to the UE. The neural network plane may determine theantenna combining information to minimize the biggest correlation valuebetween the effective channels. Or, it may determine to make the biggestcorrelation value between the effective channels less than a thresholdvalue. By doing this, the base station may prevent the antenna groupsfrom being aligned in the same direction. In another example, supposethere are 2 UEs (UE a and UE b) and that the UE a has lots of data to betransmitted/received while there are little for UE b. In this case, theneural network provides more effective channels to UE a while UE b getsfewer number of effective channels. In another example, the UE maydetermine the preferred antenna combining method based on the ACK/NACKof the received data. When the number of effective channels increases,the more diversity gain can be acquired. So, the UE of this examplerequest more number of effective channels when the decoding results ofthe received data is NACK for certain number of time. Otherwise, the UEmay request less number of effective channels. In still another example,the UE may determine the preferred antenna combining method based on theestimated channel information. The above preferred antenna combiningmethods of the UE can be controlled and granted by the network. Theneural network may consider not only the UE transmitted this preferredantenna combining method, but other UEs within the cell.

In one implementation, FIG. 7B shows an exemplary learning machine toautomatically adjust the position/aim of the antennas to optimize datatransmission performance and/or coverage. As noted earlier, 4G systemshave range but lack speed. 5G systems have speed but requires moreantennas and generally lacks the range of 4G systems. To optimizeperformance, a learning machine is used to automatically track a mobiledevice and adjust the best arrangement for the antenna arrays. Theprocess is as follows:

Collect performance data from subsystems (see above) such as: Spatialand Modulation Symbols, RSSI, TSSI, CSI (channel state information), andattributes on channel matrix and error vector magnitude, for example

Extract features and train learning machine to optimize spectralefficiency and energy efficiency of the wireless system

During live communication, extract features from live 5G data and selectantenna orientation/setting/params based on client device, resourcesavailable, and tower network properties for optimum transmission.

FIGS. 7C-7D show exemplary learning machine details. While the learningmachine optimizes all resources, details on the antenna are discussednext, with the expectation that other resource allocations. The learningmachine turn the antenna arrays “smart” so that the best antenna linkagebetween transceivers is achieved. Further, when one of the antennaelements in the array fails, the beamforming and beamsteeringperformance of the array degrades gracefully. Such an objective isachieved by reconfiguring the array when an element is found to bedefective, by either changing the material properties of the substrateor by applying appropriate loading in order to make the array functionalagain. One embodiment changes the excitation coefficient for each arrayelement (magnitude and phase) to optimize for changes due to theenvironment surrounding an array antenna. Using learning machines, onecan train the antenna array to change its elements' phase or excitationdistribution in order to maintain a certain radiation pattern or toenhance its beamsteering and nulling properties and solve the directionof arrival (DOA) as well.

The neural network control of the MIMO antennas provides significantgains that offer the ability to accommodate more users, at higher datarates, with better reliability, while consuming less power. Using neuralnetwork control of large number of antenna elements reduces power in agiven channel by focusing the energy to targeted mobile users usingprecoding techniques. By directing the wireless energy to specificusers, the power in channel is reduced and, at the same time,interference to other users is decreased.

In addition to controlling the 5G operation, the neural network can beused to provide local edge processing for IOT devices. A strikingfeature about neural networks is their enormous size. To reduce size ofthe neural networks for edge learning while maintaining accuracy, thelocal neural network performs late down-sampling and filter countreduction, to get high performance at a low parameter count. Layers canbe removed or added to optimize the parameter efficiency of the network.In certain embodiments, the system can prune neurons to save some space,and a 50% reduction in network size has been done while retaining 97% ofthe accuracy. Further, edge devices on the other hand can be designed towork on 8 bit values, or less. Reducing precision can significantlyreduce the model size. For instance, reducing a 32 bit model to 8 bitmodel reduces model size. Since DRAM memory access is energy intensiveand slow, one embodiment keeps a small set of register files (about 1KB) to store local data that can be shared with 4 MACs as the leaningelements). Moreover, for video processing, frame image compression andsparsity in the graph and linear solver can be used to reduce the sizeof the local memory to avoid going to off chip DRAMs. For example, thelinear solver can use a non-zero Hessian memory array with a Choleskymodule as a linear solver.

In another embodiment, original full neural network can be trained inthe cloud, and distillation is used for teaching smaller networks usinga larger “teacher” network. Combined with transfer learning, this methodcan reduce model size without losing much accuracy. In one embodiment,the learning machine is supported by a GPU on a microprocessor, or toreconfigure the FPGA used as part of the baseband processing as neuralnetwork hardware.

FIG. 8 shows an exemplary hybrid classical/quantum computer supportingedge computing near the antenna towers. FIG. 8 shows an exemplary ablock diagram of a system 810 for hybrid classical and quantumprogramming according to one or more embodiments described herein.Repetitive description of like elements employed in other embodimentsdescribed herein is omitted for sake of brevity.

A hybrid classical quantum computer with neural network code translatoris presented.

In one aspect, a system includes a neural network 81 that receivesclassical specification and data and determines if a portion of suchspecification is best suited to run on a quantum computer and if soselects a conversion module 812 that maps classical to quantumalgorithm; the modified code is provided to an execution unit 814 thatselects one or more quantum computers 816 or classicalCPU/GPU/neuromorphic processors 818. Also, the neural network maps thedata to a cryogenic memory 817 or use by either computers 816 or 818.The result is received by the execution unit 814 and mapped to apredetermined format as output using mapper 820.

In one aspect, a system includes a neural network 10 that receivesclassical specification and data and determines if a portion of suchspecification is best suited to run on a quantum computer and if soselects a conversion module 12 that maps classical to quantum algorithm;the modified code is provided to an execution unit 14 that selects oneor more quantum computers 16 or classical CPU/GPU/neuromorphicprocessors 18. Also, the neural network maps the data to a cryogenicmemory 17 or use by either computers 16 or 18. The result is received bythe execution unit 14 and mapped to a predetermined format as outputusing mapper 20.

In FIG. 8, classical code is provided to a learning machine such asneural network 81 that is trained to map code from classical code to amatching quantum algorithm module 812. The classical code can be a highlevel language such as Ada, Fortran, Java, C, or pseudo-code, forexample. A parser can reduce a generalized computing problem to a basecomputing problem. For instance, the problem parser can utilizereduction to translate a non-3-SAT NP-complete problem to a 3-SATinstance. Other implementations are also possible.

In one aspect, a system includes a neural network 810 that receivesclassical specification and data and determines if a portion of suchspecification is best suited to run on a quantum computer and if soselects a conversion module 812 that maps classical to quantumalgorithm. One classical to quantum algorithm module 812 reduces a firstcode of a problem type, e.g., a first NP-complete problem, to a secondNP-complete problem associated with a quantum circuit or code.

The modified code is provided to an execution unit 814 that selects oneor more quantum computers 816 or classical CPU/GPU 818. Also, the neuralnetwork maps the data to a cryogenic memory 817 or use by eithercomputers 816 or 818. Cryogenic memory 817 can be a superconductivedevice such as the Josephson junction memory device of Smith ApplicationNo. 20190190463, the content of which is incorporated by reference.Memory 817 is much faster than conventional memory and provides for dualports for dual access. A classical, that is, non-quantum, centralprocessing unit (CPU) typically consists of a control unit and adatapath, both of which are typically implemented using digitalcomplementary metal-oxide-semiconductor (CMOS) circuitry. The controlunit typically translates the program's instructions to decide how tomanipulate the data, and manages the resources in the CPU necessary forthe execution of the instruction, as well as the timing of eachinstruction execution. The datapath is a collection of functional units,registers, and buses in the processor where the data being processedflow through. The computation is carried out by the control unitinstructing the various functional units, registers, and buses tomanipulate the data, resulting in the final output of the desiredcomputational task. In a typical CPU, the control unit and the datapathare implemented using digital circuits constructed using logic elements,built using transistors, and are highly intertwined in its layout on thechip. A quantum computer (or QC/QIP system) manipulates quantum data(measured in units of qubits), and therefore the datapath has to be madeup of quantum objects. The functional units that store, transport, andmanipulate the data must be able to handle the qubits, maintainingquantum characteristics (such as superposition and entanglement) andacting simultaneously on all components of the superposition inputstates. On the other hand, the control unit is typically classical, asthe instructions specified in the control program are classical innature. A typical control unit can be configured to translate aninstruction from a program or algorithm into a classical control signalthat operates the functional units to act on the qubits to effect thedesired data manipulation. The action on the qubit is generally analogin nature, where the classical control signal (typically consists of acarrier electromagnetic field (e.g., radio frequency (RF), microwave,optical) with modulation that encodes the action in either the same or aseparate field) transforms the qubit (or a group of qubits) to adifferent quantum state via controlled time-evolution of quantumsystems. That is, the classical control signal is used to controloperations that sequentially transform the qubit states over time togenerate the desired computation or simulation.

The result is received by the execution unit 814 and mapped to apredetermined format as output using mapper 820. The execution unit 814that can facilitate execution of the quantum circuit associated with thesecond computing problem at a quantum computer 816. In an aspect, theexecution component 816 can facilitate operation of the quantum computer816 by providing quantum circuit information associated with the secondcomputing problem to the quantum computer 816, as described inaccordance with various aspects herein. Execution of the secondcomputing problem at the quantum computer 816 can result in a firstoutput corresponding to the second computing problem constructed by themodule 812. The system 1 further includes a mapper 820 that can map thefirst output obtained by the quantum computer 816 via the execution unit814 to a second output that corresponds to the original computingproblem provided to the module 812. The module 812 can be a quantumsolver to leverage various properties of computational complexity, e.g.,that NP-complete problems can be reduced to each other in polynomialtime, to program a quantum computer to solve a base NP-complete problem,thereby enabling programming of quantum computers to solve NP-completeproblems by first reducing them to the base NP-complete problem.

One example quantum NP-complete solution module 812 exploits reductionsuch that all NP-complete problems can potentially be solved by a corequantum solver that is directed to a single NP-complete problem, sinceall other NP-complete problems can be solved by using a reductionwrapper around the core solver according to properties of NP-completeproblems as known in the art. By designing the framework in this wayshown by diagram 8200, the difficulty associated with constructingquantum models and/or encodings for a whole array of differentNP-complete problems can be circumvented, as a quantum solution for agiven NP-complete problem can be utilized to find solutions forrespective other NP-complete problems.

One module 812 generates configuration data for the oracle markingsubcircuit by converting respective elements of the second computingproblem via at least one of Pauli-X quantum gates or N-th ordercontrolled-not (CNX) quantum gates. The module generates configurationdata for a CNX quantum gate by combining a number N ofcontrolled-controlled-not (CCX) quantum gates and respectivelycorresponding ancillary qubits. The module generates configuration datafor the amplitude amplification subcircuit based on a transformationmatrix. The input mapping component maps the input of the firstcomputing problem to the input of the second computing problem based ona reduction mapping; and the computer executable components furthercomprise an output mapping component that maps the first output to thesecond output based on the reduction mapping.

One module 812 performs Grover's Search algorithm, which exploits thenature of quantum superposition and achieves searching through anunordered list of N items. The Grover's Search algorithm is described inmore detail below. Various embodiments of module 812 described hereincan utilize a generalized formulation for the Grover's Search algorithmin connection with a corresponding Boolean satisfiability (SAT) solverto implement a fully automated solution toolkit. It should beappreciated, however, that the various embodiments shown and describedherein could be modified, extended, and/or otherwise configured toutilize any other suitable algorithm(s) for any other suitable basecomputing problem(s). The Grover's Search algorithm accepts an unorderedcollection of N items and a binary oracle function ƒ(⋅) that indicateswhether an item meets the search criterion. Accordingly, if a randomitem i is selected from the collection, ƒ(i)==1 would indicate a hitwhile ƒ(i)==0 would indicate that further searching is needed. In themodule 812, a single ƒ query can have the effect of checking on multipleitems all at once because qubits can be put in superpositioned states.For example, if n=log 2 N qubits are placed into a uniformsuperposition, then a single application of ƒ can be used to check all Nitems at once. Grover's algorithm can be utilized as follows. Initially,a set of n qubits are utilized to encode N=2n possible states (items),of which |ψ> is the search target. First, the n qubits are put inuniform superposition utilizing Hadamard gates as described above. Next,a marking step is carried out, in which the amplitude of the targetstate is flipped while leaving the amplitudes of the other statesunchanged. This can done by constructing a unitary operator Uƒ, suchthat Uƒ|x,y>=|(x,ƒ(x)⊕y), where ⊕ represents binary XOR, x representsthe state of the n qubits used to encode the N items and is initializedto be in the uniform superposition of all N items, and y is an ancillary(helper) qubit, prepared by feeding |I> through a Hadamard gate, asfollows:

$\left. y \right\rangle = {{H\left. 1 \right\rangle} = {\frac{\left. 0 \right\rangle - \left. 1 \right\rangle}{\sqrt{2}}.}}$

At this stage, the amplitude for the target state ψ is flipped, and theamplitudes of all other states remain unchanged, thereby marking thetarget state. Grover's algorithm causes the target state to stand outvia an additional amplitude amplification step, which can beaccomplished via inversion about the mean. A unitary operation Mn, whichcan be given as:

$M_{n} = {\begin{bmatrix}{1 - \frac{2}{2^{n}}} & {- \frac{2}{2^{n}}} & \ldots & {- \frac{2}{2^{n}}} \\{- \frac{2}{2^{n}}} & {1 - \frac{2}{2^{n}}} & \ldots & {- \frac{2}{2^{n}}} \\\vdots & \vdots & \ddots & \vdots \\{- \frac{2}{2^{n}}} & {- \frac{2}{2^{n}}} & \ldots & {1 - \frac{2}{2^{n}}}\end{bmatrix}.}$

To verify the rotation-about-the-mean effect, Mn can be applied to analready marked (last item) state vector. Grover's Search algorithmincludes a marking step and an amplification step. Accordingly, thesystem can generate configuration data for a Grover's Search quantumcircuit that includes an oracle marking subcircuit and an input mappingcomponent amplitude amplification subcircuit, each of which aredescribed in further detail below.

Oracle Marking Implementation is discussed next. The marking operationUƒ|x, y>=|x, ƒ(x)⊕y> in Grover's algorithm is determined by the booleanoracle function ƒ, which takes as input a single quantum state andoutputs whether or not the state is a search target. In an aspect, theoracle function ƒ for a SAT problem can be set to a Boolean CNFexpression itself, where the 2n possible states respectively correspondto a particular assignments to the n boolean variables.

Based on this direct mapping between the 3-SAT problem itself and theoracle function, the Boolean operations associated with respective CNFs,e.g., NOT (♭), OR (v), and AND ({circumflex over ( )}) can be definedfor implementation on a quantum computer as follows.

A NOT operator can be configured to switch between the |0> and |1>states, or more generally, the α|0>+β|1> and β|0>+α|1> states to accountfor quantum superposition. As this is the operation of the quantumPauli-X gate as described above, a NOT operator can simply berepresented as a Pauli-X gate.

For the OR operator, since De Morgan's law provides that v1 v v2⇔¬(¬v1{circumflex over ( )}=≙v2), OR operations can be converted into ANDoperations with the help of the NOT operator, which can be implementedvia a Pauli-X gate as noted above.

In an aspect, the logical AND operator can be implemented by leveragingCCX gates as described above. As noted above with respect to FIG. 4, the3-qubit CCX (Toffoli) gate flips the state of the last qubit |0>↔|1>) ifand only if the first two input qubits are both |1>. Accordingly, byconfiguring the first two qubits |q0> and |q1> to hold the problemvariables and introducing an ancillary (helper) qubit |q2>=|0> as thelast qubit, the corresponding CCX operation results in |q2> being in thestate representing the AND of |q0> and |q1>. In an aspect, the CCX gatecan be extended to achieve a CNX gate (e.g., CC . . . CX for N instancesof C, or CNX), thereby enabling logical AND operations for more than twovariables. which would then be able to handle the ANDing of an arbitrarynumber of variables.

Multiple CCX gates illustrated by diagram 700 can be combined to createa CNX gate by combining the multiple CCX gates and introducingrespectively corresponding ancillary qubits, where each single CCXbrings a new variable into the collective AND, and the ancillas helphold intermediate states.

In an aspect, due to quantum entanglement, the CCX gates on the rightside of the V are utilized even after obtaining the desired state of |r>In other words, even though |r> is in our desired state at the valley ofthe V, the helper |α1>s are entangled with the variable qubits |vi> andthe result qubit |r>. As a result, intermediate values they hold cannotbe abandoned, because doing so would cause the states of the ancillaryqubits to collapse, in turn affecting the states of the variable qubits|vi> and the result qubit |r>. Therefore, the CCX gates on the rightside of the V are utilized to clean up the ancillas, thereby reversingthem back to their initial |0> state and disentangling them from theother qubits.

Another module 812 enables quantum annealing adapted to simultaneouslytrack configurations in a superimposed state in order to obtain minimumenergy (or costs) finally desired in quantum computing, and employs anadiabatic quantum computation (AQC) technique particularly in order toperform quantum annealing. Furthermore, AQC employs a technique forfinally obtaining a solution in a desired target state by generating anadiabatic change of a Hamiltonian from an initial state to the targetstate.

Another module 812 solves pattern recognition problems done by complexdeep neural network through adiabatic evolution using a quantum system.The quantum computer operates singly or in combination with classicalcomputers provide sufficiently desirable quality to real-time patternrecognition applications. In this module, the relationships between theentries of data can be represented by respective vectors and thesimilarity between grouped patterns represented by combinations ofvectors is analyzed. In connection with the combinations of vectors, forexample, in the case of news, the relationships between pieces of text,i.e., contexts, are extracted, and may be provided as vectorizedrepresentations using a classical computer and then processed by quantumcomputers. In the case of voice data, changes in the frequencycomponents over time may be extracted using classical computers, and maybe provided as vectorized representations. In this case, the vectors maybe modeled as the physical state variables of the quantum system. Themodule 812 can perform reading and diagnosis of medical images,applications designed to predict the effectiveness of a new medicine ordetect a side effect in advance in the pharmaceutical industry, patternrecognition for the planning of marketing or automation of customermanagement, and applications designed to extract significant informationfrom a massive amount of data obtained via the Internet of Things andanalyze the extracted information.

Another module 812 handles machine vision for recognizing patternsbetween images by using a quantum system, the machine vision apparatusincluding: an optical module configured to acquire a first image; aprocessor configured to derive a first pattern from the relationshipsbetween points of interests of the acquired first image, and to derive asecond pattern from the relationships between points of interests of asecond image; and memory configured to store the derived first andsecond patterns; wherein the processor includes: a modeling unitconfigured to set up an objective function based on the similaritybetween the first pattern and the second pattern stored in the memory;and an interpretation unit configured to find an optimum first patternand an optimum second pattern, in which the similarity between the firstpattern and the second pattern is optimized, by interpreting a finalquantum state obtained through an adiabatic evolution process of thequantum system in which the objective function is optimized. Theprocessor may be further configured to: vectorize the relationshipsbetween the points of interests of the first image, and generate thefirst pattern by modeling a set of the vectorized relationships betweenthe points of interests of the first image as the first pattern; andvectorize the relationships between the points of interests of thesecond image, and generate the second pattern by modeling a set of thevectorized relationships between the points of interests of the secondimage as the second pattern. The quantum system may include a physicalmodel that depends on interaction between dipoles.

Another module 812 recognizes patterns between images in machine visionwith a quantum system by setting up an objective function based on thesimilarity between a first pattern derived from the relationshipsbetween points of interests of a first image and a second patternderived from the relationships between points of interests of a secondimage; and finding an optimum first pattern and an optimum secondpattern, in which the similarity between the first pattern and thesecond pattern is optimized, by interpreting a final quantum stateobtained through an adiabatic evolution process of the quantum system inwhich the objective function is optimized. The setting up may include:vectorizing the relationships between the points of interests of thefirst image, and modeling a set of the vectorized relationships betweenthe points of interests of the first image as the first pattern; andvectorizing the relationships between the points of interests of thesecond image, and modeling a set of the vectorized relationships betweenthe points of interests of the second image as the second pattern. Thequantum system may include a physical model that depends on interactionbetween dipoles, or may include an Ising model that depends on dipoleinteraction of a magnetic body.

Another module 812 solves cryptographic problems. Public keycryptosystems may also be used to establish a key that is shared betweentwo devices. In its simplest form, as proposed by Diffie-Hellman, eachdevice sends a public key to the other device. Both devices then combinethe received public key with their private key to obtain a shared key.One device, usually referred to as an entity (or correspondent), Alice,generates a private key ka and sends another device, or entity, Bob, thepublic key kaP. Bob generates a private key kb and sends Alice thepublic key kbP. Alice computes ka·kbP and Bob computes kb·kaP so theyshare a common key K=kakbP=kbkaP. The shared key may then be used in asymmetric key protocol. Neither Alice nor Bob may recover the privatekey of the other, and third parties cannot reconstruct the shared key.One of the important abilities of quantum computers is to efficiently,which means in polynomial time, factor large integers and solve thediscrete logarithm problem (for example, given g and h=gx in group G,find x). A significant factor affecting cryptography's security is basedon these two mathematical problems, which are considered to be safe inthe realm of classical computing. This means that with the appearance ofquantum computers, classical cryptosystems may no longer be safe. Thefield of ‘post-quantum cryptography’ is involved in developingcryptosystems for classical computers so that the classical computersystems would be quantum-resistant and secure against possibleadversaries employing quantum computing. In some contexts, fullyhomomorphic encryption allows data to be encrypted by one party andprocessed by another. The requirements of fully homomorphic encryptioncan be relaxed, for example, by allowing other interactions between theclient and server. At the same time, the requirements can bestrengthened, for example, by asking for information-theoretic security.This can produce an asymmetric scenario—a quantum server or quantumcloud architecture, which is a particularly relevant scenario in manycomputing environments. In some implementations, a client (e.g., apartially-classical client) can delegate the execution of any quantumcomputation to a remote quantum server, and this computation can beperformed on quantum data that is encrypted via a quantum one-time pad.Privacy can be maintained, for example, where the server is not providedaccess to the encryption key or under other conditions. In such adelegation of computation on encrypted data, the operations performed bythe client can be significantly easier to perform than the computationitself. For example, in some cases, the client has only limited quantumresources, rather than universal quantum computation capabilities. Insome cases, the client has the ability to perform encryption anddecryption, e.g., by applying single-qubit Pauli operators; the clientcan also have the ability to prepare and send random qubits chosen froma set of four possibilities. The set of four possible states can beunitarily equivalent to the set, which are known as the BB84 states, forthe role that they play in the quantum key distribution protocol knownby the same name. In some cases, the client does not use quantum memory.For example, auxiliary quantum states can be prepared using photonpolarization or other suitable techniques. In such cases, for an honestclient, security can be proven against any cheating server viasimulations. The protocols described here may provide advantages in someinstances. For some protocols, a conceptually simple proof ofcorrectness is available, together with a security definition and proofthat is applicable to all types of information, including sharedentangled quantum registers. Additionally, some protocols may be moreefficient in terms of quantum or classical communication, and could leadto the experimental delegation of a wider class of private quantumcomputations.

The module 812 can be a quantum encryption component that employs amethod wherein qubits are established via an oscillating polarizationfunction generated as a function of the time-varying electric field of alight wave, where the relationship between the polarization and appliedelectric field is linear, with the resulting time-varying polarizationsinusoidal at frequency φ1 through ωn. In this way, sinusoidally-varyingfields are generated for any medium in which induced polarization is anonlinear function of any electric field, inducing polarizations thatincorporate frequency components at 1-to-n higher harmonics of theoriginal (first-order, or linear) frequency, generating the basis for1-to-n qubit encryption, where Σ-frequencies of second-to-nth-orderharmonic waves expressing as qubits are calculated using perturbationsof Maxwell's equations for static and time-varying electric and magneticfields, and where computational reversibility may be calculated throughan Inverse Fourier Transform on any classical (non-quantum) functionƒ(x). The quantum encryption enables computational reversibility thatcan be calculated through a Quantum Fourier Transform (QFT) as thediscrete Fourier transform with a specified decomposition into a productof simpler unitary matrices. The quantum encryption enables a methodwherein the relationship between induced polarization P and the electricfield E is not linear; qubits are established as a result of thegenerated polarization not being the same for a given applied field ofmagnitude +E0, in the same fashion as for an applied field of magnitude−E0. In either case, the polarization response to any given appliedsinusoidal field is not purely sinusoidal, generating a distortionreflecting the presence of polarization components at frequencies ≠ω1,and therefore providing the basis state for a strong component at thesecond-harmonic frequency 2ω1, and at nth-harmonic frequencies 2ωn. Thequantum encryption enables sending-receiving parties to rotateEinstein-Podolsky-Rosen (EPR) quantum-key-generating pairs by 1-to-nspin-polarized phase angles (θ) throughout the integer and/orsub-integer range 0≤θ≤360. The protocols described here can be used in avariety of applications. For example, a delegated, private execution ofShor's algorithm can be used to factor in polynomial time on a quantumcomputer (factoring in polynomial time is widely believed to beintractable on a classical computer). Where the computation is performedon an encrypted input, the server will not know which integer he isfactoring; if this integer corresponds to an RSA public key then theserver will not know which public key he is helping to break. Thus,quantum computing on encrypted data may be useful, for example, for thedelegation of problems that can be solved in quantum polynomial time,with the underlying assumption that they cannot be solved in classicalpolynomial time. The protocols described here can also be useful inother suitable applications and scenarios. Other modules 812 can beimplemented.

In one embodiment, the system takes as input a problem formation asdefined by a problem parser and automatically generates thecorresponding quantum program that solves the parsed problem. Forinstance, the quantum code generator component can take as input a 3-SATproblem formation and generate a quantum program that solves the 3-SATproblem as described above. Other implementations could also be used. Inan aspect, a quantum program generated by the quantum code generatorcomponent can be generated in any suitable quantum programming language,such as the Open Quantum Assembly Language (OpenQASM) format and/orother suitable formats.

A toolkit can additionally include one or more interfaces to facilitateinteraction between a human user and the components of system. Forinstance, a graphical Web interface can be provided for accessing thetoolkit in an interactive manner. The interface and/or relatedcomponents can also or alternatively provide tutorials and/orexplanations regarding quantum algorithms, computing problems, quantumcomputing in general, and/or other topics. As another example, theinterface and/or related components can provide for visualization ofarbitrarily complex quantum circuits. In another aspect, an applicationprogramming interface (API) and/or other means can be utilized to alterand/or extend the functionality of system in various ways. For instance,the problem parser can be modified via the API to expand and/orotherwise alter the scope of computing problem types that can be reducedto the base problem type utilized by the quantum code generatorcomponent. Also or alternatively, the quantum code generator componentcan be modified via the API to expand and/or otherwise alter the baseproblem types and/or algorithms that can subsequently be passed onto thequantum processor for execution. In the event that the quantum codegenerator component can operate according to multiple base problem typesand/or quantum algorithms, the quantum code generator can select a baseproblem and/or algorithm to use for a given input problem based on amapping between respective input problems and reduction mappings and/orbased on other criteria.

The system 1 provides an end-to-end framework for bringing the potentialpower of classical computers and quantum computers in a generalizedmanner to software engineering researchers and practitioners. Further,the framework uses learning machine to circumvent the significantdifficulty associated with modeling, encoding, and solving multipledifferent NP-complete problems on classical or quantum computers. Thelearning machine automatically translates the high level instruction orcode specifying the problem to be solved and selects a combination ofclassical/quantum computers to solve the task. Moreover, the system 1can be provided at the edge so provide high performance low latencycloud compute solutions at a low cost as compute resources can be sharedby many devices.

In connection with the use of quantum computers, there are manydifferent hardware and software approaches. One hardware approach is touse dies or integrated circuits made of a superconductive material, suchas aluminum or niobium. The qubits can also be made from (1) ion trapsfor capturing single atoms and (2) Josephson junction-basedsuperconducting circuits.

In ion trap embodiment, a plurality of control units are provided asindependently programmable and can be used to control a distinct set ofqubits. The number of qubits can be the same for all of the controlunits or can vary across control units, depending on the programminginstructions received and the maximum number of qubits that can beloaded or enabled by a respective one of the control units. Each controlunit handles qubits in a different region of the ion trap. It ispossible then to expand a software-defined quantum computer by how thesecontrol units are used in connection with adjacent regions in an iontrap. The ion trap can perform “shuttling” of ions or atoms betweendifferent control units (e.g., between different regions in an iontrap). In one example, if both control units initially handled thirty(30) ions or atoms, and five (5) ions or atoms are shuttled ortransferred over, then one control unit is left controlling or handlingthirty five (35) ions or atoms and the other control unit is leftcontrolling or handling twenty five (25) ions or atoms. These shuttledor transferred ions or atoms can be used to communicate information fromone set of qubits to another set of qubits.

In one software-defined quantum computer can include first and secondcontrol units, where the first control unit is configured to receiveprogramming instructions (e.g., programming instructions) from executionunit 814 and generate first control signals and a first plurality ofqubits is enabled and controlled (e.g., x qubits) by the first controlsignals from the first control unit, and where the second control unitis configured to receive programming instructions from the executionunit 814 and generate second control signals and a second plurality ofqubits is enabled and controlled (e.g., y qubits) by the second controlsignals from the second control unit. In such a quantum computer, anumber of control units including the first control unit and the secondcontrol unit can be dynamically changed (e.g., increased or decreasedbased on the number of qubits needed and the number of control unitsneeded to control those qubits). The first control unit is furtherconfigured to shuttle a number of the first plurality of qubits (e.g., zqubits) to be controlled by the second control unit such that a numberof the second plurality of qubits is increased by the number of thefirst plurality of qubits that are shuttled (e.g., y+z qubits). A numberof qubits that remain under the control of the first control unit arereduced by the amount of qubits shuttled over (e.g., x−z qubits). Thenumber of the first plurality of qubits that are shuttled includesinformation associated with the first plurality of qubits, and theinformation is transferred to the second plurality of qubits by thenumber of the first plurality of qubits that are shuttled. The number ofthe first plurality of qubits shuttled to be controlled by the secondcontrol unit includes one or more qubits and the shuttling of the one ormore qubits establishes a communications channel between the firstplurality of qubits and the second plurality of qubits. The firstplurality of qubits includes memory/operations qubits and communicationsqubits that are enabled and controlled by the control signals from thefirst control unit, and the number of the first plurality of qubits thatare shuttled includes one or more of the communication qubits. It ispossible to expand the capabilities of a software-defined quantumcomputer by adjusting the number of ions or atoms and the number ofcontrol units needed to control the ions or atoms. Some qubits may beused for memory/operations .g., qubits 130 a) and others may becommunications qubits used to enable the communications channels 225(e.g., qubits 130 b). When the qubits 130 in a module are implementedusing ion-trapped technology, for example, the memory/operation qubits130 a can be based on 8171Yb+ atomic ions, and the communication qubits130 b can be based on 138Ba+ atomic ions. Other species and/or isotopescan also be used for the pairs of memory/operations and communicationsqubits. The memory/operations qubits are enabled and controlled by thecontrol signals from the respective control unit, and the communicationqubits are enabled and controlled by the control signals from therespective communication control unit. The memory/operation qubits canbe based on 8171Yb+ atomic ions, and the communication qubits can bebased on 138Ba+ atomic ions. Other species and/or isotopes can also beused for the pairs of memory/operations and communications qubits. Thememory/operations qubits are enabled and controlled by the controlsignals from the respective control unit, and the communication qubitsare enabled and controlled by the control signals from the respectivecommunication control unit. Further, elasticity from cloud computing canbe done. For example, it is possible to implement an elastic computingenvironment where qubits can be shuttled from a reserve region (alreadyloaded in a separate trapping zone) to the computing region and back ondemand during the runtime of a program. That is, the demands placed onthe system during runtime of a program may be used to dynamically modify(e.g., provide elasticity to) the computing environment. Therefore, itis possible to have readily available zones in a trap with additionalcomputing resources (e.g., preloaded ions) to easily expand thecomputing environment on demand.

A quantum core may also be referred to as a quantum unit, a core unit,or simply a core. As used in this disclosure unless otherwise specified,a quantum core may mean, for example, an individual ion trap (althoughquantum cores of other technologies may also be used). It is understoodthat an individual ion trap may include one or more qubits. If the coreunits in a network or architecture are not identical, the architectureis referred to as a heterogeneous architecture (this could meandifferent ion traps, or different cores made of different technologies,such as ion traps and trapped neutral atoms, or superconducting qubits).On the other hand, when a network or architecture has identical coreunits (e.g., identical ion traps), the architecture is referred to as ahomogenous architecture.

In another embodiment, the operation of superconducting qubit-typequantum devices may be based on the Josephson effect, a macroscopicquantum phenomenon in which a supercurrent (a current that, due to zeroelectrical resistance, flows for indefinitely long without any voltageapplied) flows across a device known as a Josephson junction. Examplesof superconducting qubit-type quantum devices may include charge qubits,flux qubits, and phase qubits. Transmons, a type of charge qubit withthe name being an abbreviation of “transmission line shunted plasmaoscillation qubits,” may exhibit reduced sensitivity to charge noise,and thus may be particularly advantageous. Transmon-type quantum devicesmay include inductors, capacitors, and at least one nonlinear element(e.g., a Josephson junction) to achieve an effective two-level quantumstate system. Josephson junctions may provide the central circuitelements of a superconducting qubit-type quantum device. A Josephsonjunction may include two superconductors connected by a weak link. Forexample, a Josephson junction may be implemented as a thin layer of aninsulating material, referred to as a barrier or a tunnel barrier andserving as the “weak link” of the junction, sandwiched between twolayers of superconductor. Josephson junctions may act as superconductingtunnel junctions. Cooper pairs may tunnel across the barrier from onesuperconducting layer to the other. The electrical characteristics ofthis tunneling are governed by the Josephson relations. Because theinductance of a Josephson junction is nonlinear, when used in aninductor-capacitor circuit (which may be referred to as an LC circuit)in a transmon-type quantum device, the resulting circuit has unevenspacing between its energy states. In other classes of superconductingqubit-type quantum devices, Josephson junctions combined with othercircuit elements may similarly provide the non-linearity necessary forforming an effective two-level quantum state to act as a qubit.

In one embodiment, processes for designing and fabricatingsuperconducting dies/integrated circuits are similar to technologies andprocesses that are used for conventional integrated circuits.Lithographic processes can be for forming lateral interconnects but thelateral interconnects may be formed by wirebonding. In otherembodiments, the lateral interconnects may be formed by additivemanufacturing (e.g., three-dimensional printing or cold spraying). Asuperconducting quantum circuit may include circuitry for providingexternal control of qubit elements and circuitry for providing internalcontrol of qubit elements 302 In this context, “external control” refersto controlling the qubit elements from outside of the die that includesthe qubit elements, including control by a user of a quantum computer,while “internal control” refers to controlling the qubit elements withinthe die that includes the qubit elements 302. For example, if qubitelements are transmon qubit elements, external control may beimplemented by means of flux bias lines (also known as “flux lines” and“flux coil lines”) and by means of readout and drive lines (also knownas “microwave lines” since qubit elements are typically designed tooperate with microwave signals), described in greater detail below. Onthe other hand, internal control lines for such qubit elements may beimplemented by means of resonators. Flux bias lines, microwave lines,coupling resonators, drive lines, and readout resonators, such as thosedescribed above, together form interconnects for supporting propagationof microwave signals. Further, any other connections for providingdirect electrical interconnection between different quantum circuitelements and components, such as connections from Josephson junctionelectrodes to capacitor plates or to superconducting loops ofsuperconducting quantum interference devices (SQUIDS) or connectionsbetween two ground lines of a particular transmission line forequalizing electrostatic potential on the two ground lines, are alsoreferred to herein as interconnects. Electrical interconnections mayalso be provided between quantum circuit elements and components andnon-quantum circuit elements, which may also be provided in a quantumcircuit, as well as to electrical interconnections between variousnon-quantum circuit elements provided in a quantum circuit. Examples ofnon-quantum circuit elements that may be provided in a quantum circuitmay include various analog and/or digital systems, e.g.analog-to-digital converters, mixers, multiplexers, amplifiers, etc. Insome embodiments, these non-quantum elements may be included in acontrol die.

In some embodiments, a quantum computing assembly may include spinqubit-type quantum devices in one or more of the dies. The spinqubit-type quantum device may include a base and multiple fins extendingaway from the base. The base and the fins may include a substrate and aquantum well stack. The base may include at least some of the substrate,and the fins may each include a quantum well layer of the quantum wellstack. The total number of fins included in the spin qubit-type quantumdevice is an even number, with the fins organized into pairs includingone active fin and one read fin, as discussed in detail below. Each finsmay include a quantum well layer that may be arranged normal to thez-direction, and may provide a layer in which a two-dimensional electrongas (2DEG) may form to enable the generation of a quantum dot duringoperation of the spin qubit-type quantum device. The quantum well layeritself may provide a geometric constraint on the z-location of quantumdots in the fins, and the limited extent of the fins (and therefore thequantum well layer) in the y-direction may provide a geometricconstraint on the y-location of quantum dots in the fins. To control thex-location of quantum dots in the fins, voltages may be applied to gatesdisposed on the fins to adjust the energy profile along the fins in thex-direction and thereby constrain the x-location of quantum dots withinquantum wells.

Another hardware may be implemented based on the quantum Ising model oftrapped ion spin-phonon chains. The quantum Ising model of trapped ionspin-phonon chains is a dipole-based physical model. An optimizedsolution to a specific vector for the solution of the machine visionproblem may be obtained by matching a dipole to the specific vector forthe solution of the machine vision problem and optimizing the statevariable of a dipole through quantum-mechanical adiabatic evolution. Thedipole is a physical property having an orientation and a scaleattributable to a magnetic field or the like. The dipole is each elementof the quantum Ising model, and may be matched to a specific vector forthe solution of the machine vision problem. A Hamiltonian can be used tosolve the quantum Ising model of trapped ion spin-phonon chainsIndividual charge detection may be performed by a spin-dependentsingle-electron tunneling event through the single-shot detection of asensitive charge detector capacitively coupled to a quantum dot.

In some embodiments, a quantum computer can have one or more quantumprocessors. A quantum computer may be configured to perform one or morequantum algorithms. A quantum computer may store or process datarepresented by quantum bits (qubits). A quantum computer may be able tosolve certain problems much more quickly than any classical computersthat use even the best currently available algorithms, like integerfactorization using Shor's algorithm or the simulation of quantummany-body systems. There exist quantum algorithms, such as Simon'salgorithm, that run faster than any possible probabilistic classicalalgorithm. Examples of quantum algorithms include, but are not limitedto, quantum optimization algorithms, quantum Fourier transforms,amplitude amplifications, quantum walk algorithms, and quantum evolutionalgorithms. Quantum computers may be able to efficiently solve problemsthat no classical computer may be able to solve within a reasonableamount of time. Thus, a system disclosed herein utilizes the merits ofquantum computing resources to solve complex problems. Any type ofquantum computers may be suitable for the technologies disclosed herein.Examples of quantum computers include, but are not limited to, adiabaticquantum computers, quantum gate arrays, one-way quantum computer,topological quantum computers, quantum Turing machines,superconductor-based quantum computers, trapped ion quantum computers,optical lattices, quantum dot computers, spin-based quantum computers,spatial-based quantum computers, Loss-DiVincenzo quantum computers,nuclear magnetic resonance (NMR) based quantum computers, liquid-NMRquantum computers, solid state NMR Kane quantum computers,electrons-on-helium quantum computers, cavity-quantum-electrodynamicsbased quantum computers, molecular magnet quantum computers,fullerene-based quantum computers, linear optical quantum computers,diamond-based quantum computers, Bose-Einstein condensate-based quantumcomputers, transistor-based quantum computers, andrare-earth-metal-ion-doped inorganic crystal based quantum computers. Aquantum computer may comprise one or more of: a quantum annealer, anIsing solver, an optical parametric oscillator (OPO), or a gate model ofquantum computing.

A system of the present disclosure may include or employ quantum-readyor quantum-enabled computing systems. A quantum-ready computing systemmay comprise a digital computer operatively coupled to a quantumcomputer. The quantum computer may be configured to perform one or morequantum algorithms. A quantum-enabled computing system may comprise aquantum computer and a classical computer, the quantum computer and theclassical computer operatively coupled to a digital computer. Thequantum computer may be configured to perform one or more quantumalgorithms for solving a computational problem. The classical computermay comprise at least one classical processor and computer memory, andmay be configured to perform one or more classical algorithms forsolving a computational problem. The term “quantum annealer” and liketerms generally refer to a system of superconducting qubits that carriesoptimization of a configuration of spins in an Ising spin model usingquantum annealing.

Cost reduced versions targeted for specific application can be done. Forexample, a machine vision device for recognizing patterns between imagesusing a quantum system includes an optical module that acquires a firstimage. The optical module is the generic terms for devices which form animage of an object in a space by using reflection, refraction,absorption, interference and diffraction, i.e., the characteristics oflight radiated by the sun or an electric light, or which are used toinvestigate the characteristics of a specific object by analyzingradioactive rays emitted from the object. A processor derives a firstpattern from the relationships between points of interests of the firstimage acquired by the optical module, and derives a second pattern fromthe relationships between points of interests of a second image. In thiscase, the second image is the reference image of the first image, andrefers to an image into which the first image has been changed. Amodeling unit included in the processor sets up an objective functionbased on the similarity between the first pattern and the second patternstored in the memory. The interpretation unit finds an optimum firstpattern and an optimum second pattern, in which the similarity betweenthe first pattern and the second pattern are optimized, by interpretinga final quantum state obtained through the adiabatic evolution processof the quantum system in which the objective function is optimized. Theprocessor vectorizes the relationships between the points of interestsof the first image, models a set of the vectorized relationships betweenthe points of interests of the first image as the first pattern,vectorizes the relationships between the points of interests of thesecond image, and models a set of the vectorized relationships betweenthe points of interests of the second image as the second pattern.During quantum processing, an optimum first pattern and an optimumsecond pattern in which the similarity between the first pattern and thesecond pattern are optimized are found by interpreting a final quantumstate obtained through the adiabatic evolution process of the quantumsystem in which the objective function is optimized. In this case, thequantum system is characterized by including a physical model dependingon the interaction between dipoles, and is characterized by including anIsing model depending on the dipole interaction of a magnetic body. TheIsing model is characterized in that it is a physical model depending ontrapped ion-based spin-phonon coupling, and the quantum system mayinclude a physical model having energy corresponding to the objectivefunction. Accordingly, machine vision-related complex computationalproblem may be modeled as the interaction between relation vectorsbetween points of interest at principal points of interest, and themachine vision-related complex computational problem may be solvedthrough the modeling. The modeling of machine vision-related complexcomputational problem using the adiabatic evolution of a quantum systemimplemented to include a physical model depending on the interactionbetween dipoles, and the pattern of an image, i.e., a combination ofvectors of the image, may be modeled as the interaction between physicaldipoles. Since the optimization problem in the field of artificialintelligence or machine learning may model an optimization process as aprocess of finding the optimum state of a Hamiltonian by using theadiabatic evolution of a quantum system, the system may be applied tovarious fields.

An arbiter may provide a quantum-enabled software service by operatingone or more of the following: Breaking down (e.g., decomposing) a givenproblem into sub-problems; Identifying the sub-problems that can besolved using a quantum-ready service; Distributing tasks between theclassical and quantum-ready services, respectively, accordingly;Collecting solutions of the sub-problems from the classical andquantum-ready services, respectively; Reducing the originalcomputational tasks using the collected solutions to sub-problems; Ifthe original problem is completely solved, the system may provide anindication of the solution and terminate; otherwise, the system mayrepeat the decomposition operation for the remaining portion of thereduced problem. The operations of quantum-ready service may be based onthe technologies described elsewhere herein. On the other hand,classical service may comprise any cloud-based software serviceconfigured to address processing of expensive computational tasks byobtaining an indication of such tasks from a client; applying requiredprocesses to transform the indication of such tasks to a proper form;and submitting the indication of such tasks to one or more classicaldigital computing devices, such as computers, clusters of computers,supercomputers, etc.

In various implementations, a computing system may include parallel ordistributed computing. The quantum-ready computer and classical computermay operate in parallel. Further, parallel computing may be implementedin the quantum-ready service. For example, a quantum computer may solvemultiple computational problems in parallel in the worker farm; a singleproblem or sub-problem may be solved in parallel in the worker farm.Similarly, a classical computer may solve multiple computationalproblems in parallel; a single problem or sub-problem may be furthersolved in a parallel or distributed manner. Intelligent algorithms fordecomposition and distribution may be dynamic and problem dependent. Acapability of solving classical and quantum tasks may vary from node tonode; for example, nodes 3 and 8 may be able to solve few classicaltasks and many quantum tasks, while node 7 may be able to solve manyclassical tasks and few quantum tasks. The intelligent algorithms maycompute certain characteristics of the potential sub-problems at acertain node in the search tree. Examples of characteristics mayinclude, but are not limited to, adequacy in classical solvers, adequacyin quantum solvers, complexity (e.g., time and processor cycles) ofcomputing tasks, current computing capacity in quantum and classicalsources, and an estimated time of computed solutions. Thecharacteristics may be deterministic or probabilistically modeled. Theintelligent algorithms may have access to information about the sizerestrictions, capacity, and best-case performance modes of each of theavailable quantum and classical computing resources. The intelligentalgorithms may use information available about the quantum and classicalcomputing resources as well characteristics of potential sub-problems.The intelligent algorithms can determine whether it is advantageous todecompose the problem at a certain node of the search tree. If adecomposition takes place, the resulting sub-problems may be added tothe pool of sub-problems together with their corresponding nodes in thesearch tree. If a decomposition is not advantageous, the intelligentalgorithms may continue traversing the search tree considering all thepossible nodes, until a certain decomposition is advantageous. Based onpartial results of sub-problems received from the quantum or classicalcomputing resources, the intelligent algorithms may be able to reducethe search tree by pruning certain nodes which may not contribute to abetter solution.

It is to be appreciated that the system herein performs operations thatcannot be performed by a human (e.g., operations that are greater thanthe capability of a single human mind). For example, an amount of dataprocessed, a speed of data processed and/or data types of data processedby the system 1 over a certain period of time can be greater, faster anddifferent than an amount, speed and data type that can be processed by asingle human mind over the same period of time. The system 1 can also befully operational towards performing one or more other functions (e.g.,fully powered on, fully executed, etc.) while also performing theoperations described herein. Moreover, quantum circuit configurations,quantum code, and/or other outputs of the system 1 can includeinformation that is impossible to obtain manually by a user in a usefulor reasonable amount of time. Additionally, it is to be appreciated thatthe system 1 can provide various advantages as compared to conventionalquantum programming tools. For instance, the system 1 can reduce theaccessibility and practicality constraints noted above with respect toquantum computing by providing an end-to-end quantum computing frameworkfor solving NP-complete problems and/or other problems via reduction. Byimplementing the quantum computing framework corresponding to the system1, toolkits and/or other aids can be provided to software engineeringresearchers and practitioners in order to enable such users to enjoy thespeedup and scalability benefits of universal quantum computers even inthe absence of prior knowledge on quantum computing. As another example,the consistency and/or accuracy quantum code generated by the system 1can be improved in relation to similar code generated by conventionaltools. Also or alternatively, the time associated with development ofquantum code for a given use case can be reduced. Other advantages canalso be realized.

Various embodiments of the present can be a system, a method, anapparatus and/or a computer program product at any possible technicaldetail level of integration. The computer program product can include acomputer readable storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry out one ormore aspects of the present invention. The computer readable storagemedium can be a tangible device that can retain and store instructionsfor use by an instruction execution device. The computer readablestorage medium can be, for example, but is not limited to, an electronicstorage device, a magnetic storage device, an optical storage device, anelectromagnetic storage device, a semiconductor storage device, or anysuitable combination of the foregoing. A non-exhaustive list of morespecific examples of the computer readable storage medium can alsoinclude the following: a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), a static randomaccess memory (SRAM), a portable compact disc read-only memory (CD-ROM),a digital versatile disk (DVD), a memory stick, a floppy disk, amechanically encoded device such as punch-cards or raised structures ina groove having instructions recorded thereon, and any suitablecombination of the foregoing. A computer readable storage medium, asused herein, is not to be construed as being transitory signals per se,such as radio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device. Computer readable programinstructions for carrying out operations of one or more embodiments ofthe present invention can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions can executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer can be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection can be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) can execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform one or more aspects of the presentinvention.

One or more aspects of the present invention are described herein withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems), and computer program products according to one ormore embodiments of the invention. It will be understood that each blockof the flowchart illustrations and/or block diagrams, and combinationsof blocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer readable program instructions. These computerreadable program instructions can be provided to a processor of ageneral purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions, which execute via the processor of the computer orother programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionscan also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks. The computer readable program instructions can also be loadedonto a computer, other programmable data processing apparatus, or otherdevice to cause a series of operational acts to be performed on thecomputer, other programmable apparatus or other device to produce acomputer implemented process, such that the instructions which executeon the computer, other programmable apparatus, or other device implementthe functions/acts specified in the flowchart and/or block diagram blockor blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successioncan, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program product thatruns on a computer and/or computers, those skilled in the art willrecognize that this disclosure also can or can be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that theinventive computer-implemented methods can be practiced with othercomputer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as computers, hand-held computing devices (e.g., PDA,phone), microprocessor-based or programmable consumer or industrialelectronics, and the like. The illustrated aspects can also be practicedin distributed computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. However, some, if not all aspects of this disclosure can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

It should also be appreciated that, while the antenna system of thepresent invention is primarily intended for 5G/6G systems, it can beused in space-borne communication applications, radar, as well as otherterrestrial applications, or in any application requiring a large,lightweight, stowable antenna.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component can be, butis not limited to being, a process miming on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution and a component canbe localized on one computer and/or distributed between two or morecomputers. In another example, respective components can execute fromvarious computer readable media having various data structures storedthereon. The components can communicate via local and/or remoteprocesses such as in accordance with a signal having one or more datapackets (e.g., data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems via the signal). As anotherexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by a software or firmware applicationexecuted by a processor. In such a case, the processor can be internalor external to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts, wherein the electroniccomponents can include a processor or other means to execute software orfirmware that confers at least in part the functionality of theelectronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. As used herein, the terms “example”and/or “exemplary” are utilized to mean serving as an example, instance,or illustration. For the avoidance of doubt, the subject matterdisclosed herein is not limited by such examples. In addition, anyaspect or design described herein as an “example” and/or “exemplary” isnot necessarily to be construed as preferred or advantageous over otheraspects or designs, nor is it meant to preclude equivalent exemplarystructures and techniques known to those of ordinary skill in the art.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of user equipment. A processor can also beimplemented as a combination of computing processing units. In thisdisclosure, terms such as “store,” “storage,” “data store,” datastorage,” “database,” and substantially any other information storagecomponent relevant to operation and functionality of a component areutilized to refer to “memory components,” entities embodied in a“memory,” or components comprising a memory. It is to be appreciatedthat memory and/or memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory. By way of illustration, and not limitation,nonvolatile memory can include read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). Volatile memory can include RAM, which canact as external cache memory, for example. By way of illustration andnot limitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM),direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), andRambus dynamic RAM (RDRAM).

Additionally, the disclosed memory components of systems orcomputer-implemented methods herein are intended to include, withoutbeing limited to including, these and any other suitable types ofmemory.

What has been described above include mere examples of systems andcomputer-implemented methods. It is, of course, not possible to describeevery conceivable combination of components or computer-implementedmethods for purposes of describing this disclosure, but one of ordinaryskill in the art can recognize that many further combinations andpermutations of this disclosure are possible. Furthermore, to the extentthat the terms “includes,” “has,” “possesses,” and the like are used inthe detailed description, claims, appendices and drawings such terms areintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim. The descriptions of the various embodiments have been presentedfor purposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Various modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

All patents, published patent applications and other referencesdisclosed herein are hereby expressly incorporated in their entiretiesby reference.

While the invention has been described with respect to preferredembodiments, those skilled in the art will readily appreciate thatvarious changes and/or modifications can be made to the inventionwithout departing from the spirit or scope of the invention as definedby the appended claims.

Many of the methods are described in their most basic form, butprocesses can be added to or deleted from any of the methods andinformation can be added or subtracted from any of the above descriptionwithout departing from the basic scope of the present embodiments. Itwill be apparent to those skilled in the art that many furthermodifications and adaptations can be made. The particular embodimentsare not provided to limit the concept but to illustrate it. The scope ofthe embodiments is not to be determined by the specific examplesprovided above but only by the claims below.

What is claimed is:
 1. A system, comprising: a transceiver tocommunicate with a predetermined target; one or more antennas coupled tothe transceiver each electrically or mechanically steerable to thepredetermined target; and an edge processing module coupled to thetransceiver and one or more antennas to provide low-latency computationfor the predetermined target.
 2. The system of claim 1, comprising aquantum computer coupled to the edge processing module.
 3. The system ofclaim 1, comprising a parser that receives classical specification anddata and determines if a portion of such specification runs on a quantumcomputer, and if so maps classical specification to quantum algorithmand the modified code is provided to an execution unit that selects oneor more quantum computers, one or more classical processor, one or moregraphical processing units (GPUs), or one or more neuromorphicprocessors.
 4. The system of claim 1, wherein the processor calibrates aconnection by analyzing RSSI and TSSI and moves the antennas untilpredetermined cellular parameters are reached.
 5. The system of claim 1,wherein the edge processing module comprises at least a processor, agraphical processing unit (GPU), a neural network, a quantum computer, astatistical engine, or a programmable logic device (PLD).
 6. The systemof claim 1, wherein the edge processing module and the antenna areenclosed in a housing or shipping container, or the edge processingmodule is in a separate shipping container adjacent the antenna.
 7. Thesystem of claim 1, wherein the transceiver comprises a 5G or 6G cellulartransceiver.
 9. A system, comprising: a transceiver to communicate witha predetermined target; one or more antennas coupled to the transceivereach electrically or mechanically steerable to the predetermined target;and a beam sweeping module controlling the antenna in accordance withone of: a service level agreement, a performance requirement, a trafficdistribution data, a networking requirement or prior beam sweepinghistory.
 10. The system of claim 9, wherein the beam sweeping isdirected at a group of autonomous vehicles, a group of virtual realitydevices, or a group of devices performing similar functions.
 11. Thesystem of claim 1, comprising a neural network coupled to a controlplane, a management plane, and a data plane to optimize 5G parameters.12. The system of claim 1, comprising one or more cameras and sensors inthe housing to capture security information.
 13. The system of claim 1,comprising edge sensors including LIDAR and RADAR.
 14. The system ofclaim 1, comprising a camera for individual identity identification. 15.The system of claim 1, wherein the edge processing module streams datato the predetermined target to minimize loading the target.
 16. Thesystem of claim 1, wherein the edge processing module shares workloadwith a core processing module located at a head-end and a cloud modulelocated at a cloud data center, each processing module having increasedlatency and each having a processor, a graphical processing unit (GPU),a neural network, a quantum computer, a statistical engine, or aprogrammable logic device (PLD).
 17. The system of claim 1, comprisingan edge learning machine in a housing or shipping container to providelocal edge processing for Internet-of-Things (TOT) sensors.
 18. Thesystem of claim 17, wherein the edge learning machine uses pre-trainedmodels and modifies the pre-trained models for a selected task.
 19. Thesystem of claim 1, comprising a cellular device for a person crossing astreet near a city light or street light, the cellular device emitting aperson to vehicle (P2V) or a vehicle to person (V2P) safety message. 20.The system of claim 1, comprising a cloud trained neural network whosenetwork parameters are down-sampled and filter count reduced beforetransferring to the edge neural network.